import numpy as np

est = dict(
           deviance = 18.59164098607571,
           dispers = 1.859164098607571,
           deviance_s = 18.59164098607571,
           dispers_s = 1.859164098607571,
           deviance_p = 24.75374834715614,
           dispers_p = 2.475374834715614,
           deviance_ps = 24.75374834715614,
           dispers_ps = 2.475374834715614,
           bic = -9.740492454486454,
           nbml = 0,
           N = 17,
           ic = 3,
           k = 7,
           k_eq = 1,
           k_dv = 1,
           converged = 1,
           k_autoCns = 0,
           ll = -31.92732830809848,
           chi2 = 128.8021169250575,
           p = 2.29729497374e-25,
           rc = 0,
           aic = 4.579685683305704,
           rank = 7,
           canonical = 1,
           power = 0,
           df_m = 6,
           df = 10,
           vf = 1,
           phi = 1,
           k_eq_model = 0,
           properties = "b V",
           depvar = "executions",
           which = "max",
           technique = "nr",
           singularHmethod = "m-marquardt",
           ml_method = "e2",
           crittype = "log likelihood",
           user = "glim_lf",
           title = "Generalized linear models",
           opt = "moptimize",
           chi2type = "Wald",
           link = "glim_l03",
           varfunc = "glim_v3",
           m = "1",
           a = "1",
           oim = "oim",
           opt1 = "ML",
           varfuncf = "u",
           varfunct = "Poisson",
           linkf = "ln(u)",
           linkt = "Log",
           vce = "oim",
           vcetype = "OIM",
           hac_lag = "15",
           marginsok = "default",
           marginsnotok = "stdp Anscombe Cooksd Deviance Hat Likelihood Pearson Response Score Working ADJusted STAndardized STUdentized MODified",
           predict = "glim_p",
           cmd = "glm",
           cmdline = "glm executions income perpoverty perblack LN_VC100k96 south degree, family(poisson)",
          )

params_table = np.array([
     .00026110166569,  .00005187148786,  5.0336259178483,  4.812884279e-07,
     .00015943541766,  .00036276791372, np.nan,  1.9599639845401,
                   0,  .07781804809828,  .07940260798777,  .98004398180811,
     .32706440886796,  -.0778082038363,  .23344430003287, np.nan,
     1.9599639845401,                0, -.09493110013466,  .02291930335216,
    -4.1419714498302,  .00003443332141, -.13985210925565, -.05001009101367,
    np.nan,  1.9599639845401,                0,  .29693462055586,
     .43751760764129,  .67868038993144,  .49734039404176,  -.5605841330232,
     1.1544533741349, np.nan,  1.9599639845401,                0,
     2.3011832004524,  .42838381728481,  5.3717790159251,  7.796361708e-08,
     1.4615663470144,  3.1408000538904, np.nan,  1.9599639845401,
                   0, -18.722067603077,  4.2839791307242, -4.3702518223781,
     .00001241033322, -27.118512409818, -10.325622796337, np.nan,
     1.9599639845401,                0, -6.8014789919532,   4.146873025502,
    -1.6401464308471,  .10097472438129, -14.929200770398,  1.3262427864914,
    np.nan,  1.9599639845401,                0]).reshape(7,9)

params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()

params_table_rownames = 'income perpoverty perblack LN_VC100k96 south degree _cons'.split()

cov = np.array([
     2.690651253e-09,  1.942168909e-06,  9.445812833e-08,  4.703695025e-06,
    -6.082922480e-06, -.00008108248895, -.00013492774575,  1.942168909e-06,
     .00630477415526,  .00017467012687,  .00328093520848, -.01768604570302,
     .11117887243846, -.19441636422025,  9.445812833e-08,  .00017467012687,
     .00052529446615, -.00313545508833, -.00516707569472, -.03253594627601,
     .01688876616272,  4.703695025e-06,  .00328093520848, -.00313545508833,
     .19142165699616, -.00179497953339,  .30391667530759, -1.4489146451821,
    -6.082922480e-06, -.01768604570302, -.00516707569472, -.00179497953339,
     .18351269491151,   .3016848477378,  .36484063612427, -.00008108248895,
     .11117887243846, -.03253594627601,  .30391667530759,   .3016848477378,
     18.352477192481, -4.0741043266703, -.00013492774575, -.19441636422025,
     .01688876616272, -1.4489146451821,  .36484063612427, -4.0741043266703,
     17.196555889636]).reshape(7,7)

cov_colnames = 'income perpoverty perblack LN_VC100k96 south degree _cons'.split()

cov_rownames = 'income perpoverty perblack LN_VC100k96 south degree _cons'.split()

infocrit = np.array([
                  17, np.nan, -31.927328308098,                7,
     77.854656616197,   83.68715002459])

infocrit_colnames = 'N ll0 ll df AIC BIC'.split()

infocrit_rownames = '.'.split()

predicted = np.array([
     35.226364135742,  .16681243479252,  .98022246360779,  8.1965742111206,
     .33106967806816,  .89840310811996,  1.3118965625763,  .29945519566536,
     .11764223873615,  3.6862981319427,  .35516858100891,  .46500706672668,
     2.0823004245758,   .3434439599514,  .24561515450478,  1.0650315284729,
     .62310123443604,  .41350400447845,  1.9260421991348,  .40797635912895,
     .32057955861092,  2.4171404838562,  .36215576529503,  .31702440977097,
     1.8473218679428,   .3869916498661,  .27665960788727,  2.8643238544464,
     .43869277834892,  .55124300718307,  3.1211984157562,  .44224792718887,
     .61045408248901,   3.338207244873,  .42789322137833,  .61120104789734,
     2.5269968509674,  .42458593845367,  .45554983615875,  .89725440740585,
     .59187793731689,  .31432569026947,  .97933322191238,  .37813624739647,
     .14003194868565,  .53462094068527,  .38791963458061,  .08045063912868,
     1.9790935516357,  .31954729557037,  .20208616554737]).reshape(17,3)

predicted_colnames = 'predict_mu predict_linpred_std predict_hat'.split()

predicted_rownames = 'r1 r2 r3 r4 r5 r6 r7 r8 r9 r10 r11 r12 r13 r14 r15 r16 r17'.split()

resids = np.array([
      1.773634314537,   1.773634314537,  .29638093709946,  .29637759923935,
      .2988341152668,  .05034962296486,  .80342543125153,  .80342543125153,
     .27623143792152,  .27622014284134,  .28062695264816,  .09801965206861,
     4.6881031990051,  4.6881031990051,  3.0157172679901,   2.977787733078,
     4.0930528640747,  3.5735311508179,  .31370183825493,  .31370183825493,
      .1611547768116,  .16114975512028,  .16338862478733,  .08509942144156,
     .91769951581955,  .91769951581955,  .59656941890717,  .59618371725082,
     .63595855236053,  .44071426987648,   .9349684715271,   .9349684715271,
     .80822360515594,  .80661898851395,  .90597397089005,  .87787866592407,
     .07395775616169,  .07395775616169,  .05295527353883,  .05295492336154,
     .05329062789679,  .03839882463217, -.41714036464691, -.41714036464691,
    -.27668312191963, -.27663832902908,  -.2683065533638, -.17257598042488,
    -.84732186794281, -.84732186794281, -.68459099531174, -.68349820375443,
     -.6234148144722,   -.458675801754, -1.8643238544464, -1.8643238544464,
    -1.2799508571625,  -1.274356007576, -1.1015654802322, -.65087747573853,
    -2.1211984157562, -2.1211984157562, -1.4092296361923, -1.4021278619766,
    -1.2006615400314, -.67961025238037,  -2.338207244873,  -2.338207244873,
    -1.5136297941208, -1.5051733255386, -1.2797535657883, -.70043802261353,
    -1.5269968509674, -1.5269968509674, -1.0992211103439, -1.0954134464264,
     -.9605849981308, -.60427337884903,  .10274560004473,  .10274560004473,
     .10649761557579,   .1064917370677,  .10846894979477,  .11451110988855,
     .02066676132381,  .02066676132381,  .02081091701984,  .02081087417901,
     .02088368684053,  .02110289037228,  .46537905931473,  .46537905931473,
     .56824368238449,  .56713002920151,  .63647866249084,  .87048417329788,
    -.97909361124039, -.97909361124039, -.77151334285736, -.77000600099564,
    -.69597083330154, -.49471819400787]).reshape(17,6)

resids_colnames = 'score_factor resid_response resid_anscombe resid_deviance resid_pearson resid_working'.split()

resids_rownames = 'r1 r2 r3 r4 r5 r6 r7 r8 r9 r10 r11 r12 r13 r14 r15 r16 r17'.split()

class Bunch(dict):
    def __init__(self, **kw):
        dict.__init__(self, kw)
        self.__dict__  = self

        for i,att in enumerate(['params', 'bse', 'tvalues', 'pvalues']):
            self[att] = self.params_table[:,i]


results_poisson_none_nonrobust = Bunch(
                params_table=params_table,
                params_table_colnames=params_table_colnames,
                params_table_rownames=params_table_rownames,
                cov=cov,
                cov_colnames=cov_colnames,
                cov_rownames=cov_rownames,
                infocrit=infocrit,
                infocrit_colnames=infocrit_colnames,
                infocrit_rownames=infocrit_rownames,
                predicted=predicted,
                predicted_colnames=predicted_colnames,
                predicted_rownames=predicted_rownames,
                resids=resids,
                resids_colnames=resids_colnames,
                resids_rownames=resids_rownames,
                **est
                )

est = dict(
           deviance = 23.34969514421719,
           dispers = .8980651978545075,
           deviance_s = 23.34969514421719,
           dispers_s = .8980651978545075,
           deviance_p = 30.06164170990202,
           dispers_p = 1.156216988842385,
           deviance_ps = 30.06164170990202,
           dispers_ps = 1.156216988842385,
           bic = -67.5595014539113,
           nbml = 0,
           N = 33,
           ic = 3,
           k = 7,
           k_eq = 1,
           k_dv = 1,
           converged = 1,
           k_autoCns = 0,
           ll = -52.96941847346162,
           chi2 = 183.6836771894393,
           p = 5.59891844113e-37,
           rc = 0,
           aic = 3.634510210512826,
           rank = 7,
           canonical = 1,
           power = 0,
           df_m = 6,
           df = 26,
           vf = 1,
           phi = 1,
           k_eq_model = 0,
           properties = "b V",
           depvar = "executions",
           which = "max",
           technique = "nr",
           singularHmethod = "m-marquardt",
           ml_method = "e2",
           crittype = "log likelihood",
           user = "glim_lf",
           title = "Generalized linear models",
           opt = "moptimize",
           chi2type = "Wald",
           wtype = "fweight",
           wexp = "= fweight",
           link = "glim_l03",
           varfunc = "glim_v3",
           m = "1",
           a = "1",
           oim = "oim",
           opt1 = "ML",
           varfuncf = "u",
           varfunct = "Poisson",
           linkf = "ln(u)",
           linkt = "Log",
           vce = "oim",
           vcetype = "OIM",
           hac_lag = "15",
           marginsok = "default",
           marginsnotok = "stdp Anscombe Cooksd Deviance Hat Likelihood Pearson Response Score Working ADJusted STAndardized STUdentized MODified",
           predict = "glim_p",
           cmd = "glm",
           cmdline = "glm executions income perpoverty perblack LN_VC100k96 south degree [fweight=fweight], family(poisson)",
          )

params_table = np.array([
     .00025343868829,  .00004015414514,  6.3116444744157,  2.760858933e-10,
     .00017473800999,  .00033213936659, np.nan,  1.9599639845401,
                   0,  .09081422305585,  .06472607217881,  1.4030547505642,
     .16060051303473, -.03604654727537,  .21767499338706, np.nan,
     1.9599639845401,                0, -.09416451429381,  .01795769655821,
    -5.2436855689475,  1.574003474e-07, -.12936095279319, -.05896807579442,
    np.nan,  1.9599639845401,                0,  .27652273809506,
     .38626128010796,   .7158955669017,  .47405583598111, -.48053545953887,
      1.033580935729, np.nan,  1.9599639845401,                0,
      2.239890838384,  .36339399714255,  6.1638080320445,  7.101602988e-10,
     1.5276516917866,  2.9521299849815, np.nan,  1.9599639845401,
                   0, -18.842583191417,   3.736940161486, -5.0422491067996,
     4.600917913e-07,  -26.16685132031, -11.518315062523, np.nan,
     1.9599639845401,                0, -6.5630017977416,  3.2352486362722,
    -2.0285927097411,  .04249979172538, -12.903972605867, -.22203098961573,
    np.nan,  1.9599639845401,                0]).reshape(7,9)

params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()

params_table_rownames = 'income perpoverty perblack LN_VC100k96 south degree _cons'.split()

cov = np.array([
     1.612355372e-09,  1.270985149e-06,  8.789752394e-08, -1.636449642e-07,
    -3.213686689e-06, -.00005643188411, -.00006199883309,  1.270985149e-06,
      .0041894644197,  .00016567874308, -.00066453618021, -.00943379587945,
     .07218307550995, -.11262571631082,  8.789752394e-08,  .00016567874308,
     .00032247886568, -.00355795369216, -.00391377556228, -.01880905186772,
     .01900717143416, -1.636449642e-07, -.00066453618021, -.00355795369216,
     .14919777651064,  .02481983169552,  .26952997380446, -.95915288407306,
    -3.213686689e-06, -.00943379587945, -.00391377556228,  .02481983169552,
     .13205519715924,  .44364186152042,  -.0298149336078, -.00005643188411,
     .07218307550995, -.01880905186772,  .26952997380446,  .44364186152042,
     13.964721770527, -3.6510403528048, -.00006199883309, -.11262571631082,
     .01900717143416, -.95915288407306,  -.0298149336078, -3.6510403528048,
     10.466833738501]).reshape(7,7)

cov_colnames = 'income perpoverty perblack LN_VC100k96 south degree _cons'.split()

cov_rownames = 'income perpoverty perblack LN_VC100k96 south degree _cons'.split()

infocrit = np.array([
                  33, np.nan, -52.969418473462,                7,
     119.93883694692,  130.41438987719])

infocrit_colnames = 'N ll0 ll df AIC BIC'.split()

infocrit_rownames = '.'.split()

predicted = np.array([
     34.815238952637,  .16658315062523,  .96612107753754,  7.3026847839355,
     .32757967710495,  .78363972902298,  1.2540435791016,  .26076200604439,
     .08527097851038,  3.9734709262848,  .24942673742771,  .24720433354378,
     2.0739872455597,  .24682784080505,  .12635557353497,  1.1471545696259,
     .45427960157394,  .23673823475838,  1.7763512134552,  .27608770132065,
     .13540133833885,  2.2698366641998,  .25641229748726,   .1492355465889,
     1.6349502801895,  .27634221315384,  .12485299259424,  2.7504913806915,
     .39550569653511,  .43024495244026,   2.862185716629,  .39729079604149,
     .45176732540131,  3.5617923736572,  .39150056242943,  .54592549800873,
     2.6135795116425,  .29556328058243,  .22831618785858,    .775799036026,
     .40655690431595,  .12823067605495,  .93375068902969,  .29390665888786,
     .08065843582153,  .56681954860687,  .28863781690598,  .04722274839878,
     1.8914022445679,  .21889741718769,  .09062857925892]).reshape(17,3)

predicted_colnames = 'predict_mu predict_linpred_std predict_hat'.split()

predicted_rownames = 'r1 r2 r3 r4 r5 r6 r7 r8 r9 r10 r11 r12 r13 r14 r15 r16 r17'.split()

resids = np.array([
     2.1847612857819,  2.1847612857819,  .36650228500366,  .36649596691132,
      .3702706694603,  .06275302171707,  1.6973150968552,  1.6973150968552,
     .60597640275955,  .60585051774979,  .62808901071548,  .23242343962193,
     4.7459564208984,  4.7459564208984,  3.0897438526154,  3.0483965873718,
     4.2380628585815,  3.7845225334167,  .02652905881405,  .02652905881405,
     .01329397037625,  .01329396758229,  .01330873556435,  .00667654490098,
     .92601269483566,  .92601269483566,  .60273587703705,  .60233747959137,
     .64300429821014,  .44648909568787,   .8528453707695,   .8528453707695,
     .72065913677216,  .71955502033234,   .7962681055069,   .7434441447258,
     .22364875674248,  .22364875674248,  .16446639597416,  .16445553302765,
     .16780391335487,  .12590345740318, -.26983660459518, -.26983660459518,
     -.1828535348177, -.18284019827843,  -.1791032999754, -.11887931078672,
    -.63495022058487, -.63495022058487, -.53598040342331, -.53542107343674,
    -.49657794833183, -.38836058974266, -1.7504912614822, -1.7504912614822,
    -1.2204585075378, -1.2154930830002, -1.0554916858673, -.63642859458923,
     -1.862185716629,  -1.862185716629, -1.2788465023041, -1.2732635736465,
    -1.1007128953934, -.65061664581299, -2.5617923736572, -2.5617923736572,
     -1.617108464241, -1.6071890592575, -1.3574055433273, -.71924245357513,
    -1.6135795116425, -1.6135795116425, -1.1469231843948, -1.1426799297333,
    -.99809640645981, -.61738300323486,  .22420094907284,  .22420094907284,
     .24363535642624,  .24356025457382,  .25454398989677,  .28899359703064,
     .06624934077263,  .06624934077263,  .06777309626341,  .06777160614729,
     .06855925172567,  .07094971090555,  .43318045139313,  .43318045139313,
     .51954871416092,  .51871728897095,  .57536894083023,  .76422989368439,
    -.89140218496323, -.89140218496323,  -.7140833735466,  -.7128586769104,
    -.64815932512283, -.47129172086716]).reshape(17,6)

resids_colnames = 'score_factor resid_response resid_anscombe resid_deviance resid_pearson resid_working'.split()

resids_rownames = 'r1 r2 r3 r4 r5 r6 r7 r8 r9 r10 r11 r12 r13 r14 r15 r16 r17'.split()


results_poisson_fweight_nonrobust = Bunch(
                params_table=params_table,
                params_table_colnames=params_table_colnames,
                params_table_rownames=params_table_rownames,
                cov=cov,
                cov_colnames=cov_colnames,
                cov_rownames=cov_rownames,
                infocrit=infocrit,
                infocrit_colnames=infocrit_colnames,
                infocrit_rownames=infocrit_rownames,
                predicted=predicted,
                predicted_colnames=predicted_colnames,
                predicted_rownames=predicted_rownames,
                resids=resids,
                resids_colnames=resids_colnames,
                resids_rownames=resids_rownames,
                **est
                )

est = dict(
           deviance = 12.02863083186947,
           dispers = 1.202863083186947,
           deviance_s = 12.02863083186947,
           dispers_s = 1.202863083186947,
           deviance_p = 15.48630027479802,
           dispers_p = 1.548630027479802,
           deviance_ps = 15.48630027479802,
           dispers_ps = 1.548630027479802,
           bic = -16.30350260869269,
           nbml = 0,
           N = 17,
           ic = 3,
           k = 7,
           k_eq = 1,
           k_dv = 1,
           converged = 1,
           k_autoCns = 0,
           ll = -27.28727618329841,
           chi2 = 94.62492461274286,
           p = 3.30927661191e-18,
           rc = 0,
           aic = 4.033797198035106,
           rank = 7,
           canonical = 1,
           power = 0,
           df_m = 6,
           df = 10,
           vf = 1,
           phi = 1,
           k_eq_model = 0,
           properties = "b V",
           depvar = "executions",
           which = "max",
           technique = "nr",
           singularHmethod = "m-marquardt",
           ml_method = "e2",
           crittype = "log likelihood",
           user = "glim_lf",
           title = "Generalized linear models",
           opt = "moptimize",
           chi2type = "Wald",
           wtype = "aweight",
           wexp = "= fweight",
           link = "glim_l03",
           varfunc = "glim_v3",
           m = "1",
           a = "1",
           oim = "oim",
           opt1 = "ML",
           varfuncf = "u",
           varfunct = "Poisson",
           linkf = "ln(u)",
           linkt = "Log",
           vce = "oim",
           vcetype = "OIM",
           hac_lag = "15",
           marginsok = "default",
           marginsnotok = "stdp Anscombe Cooksd Deviance Hat Likelihood Pearson Response Score Working ADJusted STAndardized STUdentized MODified",
           predict = "glim_p",
           cmd = "glm",
           cmdline = "glm executions income perpoverty perblack LN_VC100k96 south degree [aweight=fweight], family(poisson)",
          )

params_table = np.array([
     .00025343868829,  .00005594520811,  4.5301232557793,  5.894928560e-06,
     .00014378809529,  .00036308928129, np.nan,  1.9599639845401,
                   0,  .09081422305585,  .09018031800722,  1.0070293059798,
     .31392069129295, -.08593595235267,  .26756439846436, np.nan,
     1.9599639845401,                0, -.09416451429381,  .02501975991718,
    -3.7636058301716,  .00016748080115, -.14320234263332, -.04512668595429,
    np.nan,  1.9599639845401,                0,  .27652273809507,
     .53816281293549,  .51382728692594,  .60737274844619, -.77825699307725,
     1.3313024692674, np.nan,  1.9599639845401,                0,
      2.239890838384,  .50630271729905,   4.424015044464,  9.688326910e-06,
     1.2475557472031,  3.2322259295649, np.nan,  1.9599639845401,
                   0, -18.842583191417,  5.2065333302747, -3.6190267105084,
     .00029571311817, -29.047201003062, -8.6379653797707, np.nan,
     1.9599639845401,                0, -6.5630017977417,  4.5075460479893,
    -1.4560032727052,  .14539171490364, -15.397629710457,  2.2716261149733,
    np.nan,  1.9599639845401,                0]).reshape(7,9)

params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()

params_table_rownames = 'income perpoverty perblack LN_VC100k96 south degree _cons'.split()

cov = np.array([
     3.129866310e-09,  2.467206465e-06,  1.706246053e-07, -3.176637541e-07,
    -6.238332985e-06, -.00010954424563,   -.000120350676,  2.467206465e-06,
     .00813248975588,  .00032161167774, -.00128998199687, -.01831266258952,
     .14012008775466, -.21862639048575,  1.706246053e-07,  .00032161167774,
     .00062598838631, -.00690661599067, -.00759732903266, -.03651168891971,
     .03689627396044, -3.176637541e-07, -.00128998199687, -.00690661599067,
     .28961921322663,  .04817967329131,  .52320524326798, -1.8618850102603,
    -6.238332985e-06, -.01831266258952, -.00759732903266,  .04817967329131,
      .2563424415444,  .86118714295143, -.05787604759173, -.00010954424563,
     .14012008775466, -.03651168891971,  .52320524326798,  .86118714295143,
     27.107989319261, -7.0873136260377,   -.000120350676, -.21862639048575,
     .03689627396044, -1.8618850102603, -.05787604759173, -7.0873136260377,
     20.317971374744]).reshape(7,7)

cov_colnames = 'income perpoverty perblack LN_VC100k96 south degree _cons'.split()

cov_rownames = 'income perpoverty perblack LN_VC100k96 south degree _cons'.split()

infocrit = np.array([
                  17, np.nan, -27.287276183298,                7,
     68.574552366597,   74.40704577499])

infocrit_colnames = 'N ll0 ll df AIC BIC'.split()

infocrit_rownames = '.'.split()

predicted = np.array([
     34.815238952637,  .23209382593632,  1.8754115104675,  7.3026847839355,
     .45640400052071,   1.521183013916,  1.2540435791016,  .36330956220627,
     .16552601754665,  3.9734709262848,  .34751656651497,  .47986721992493,
     2.0739872455597,  .34389564394951,   .2452784627676,  1.1471545696259,
     .63293009996414,  .45955070853233,  1.7763512134552,  .38466224074364,
      .2628378868103,  2.2698366641998,  .35724925994873,  .28969252109528,
     1.6349502801895,  .38501682877541,  .24236169457436,  2.7504913806915,
     .55104273557663,  .83518141508102,   2.862185716629,  .55352979898453,
     .87696009874344,  3.5617923736572,  .54546248912811,  1.0597376823425,
     2.6135795116425,  .41179683804512,  .44320201873779,    .775799036026,
      .5664399266243,  .24891836941242,  .93375068902969,  .40948873758316,
     .15657225251198,  .56681954860687,  .40214782953262,  .09166768193245,
     1.8914022445679,  .30498126149178,  .17592607438564]).reshape(17,3)

predicted_colnames = 'predict_mu predict_linpred_std predict_hat'.split()

predicted_rownames = 'r1 r2 r3 r4 r5 r6 r7 r8 r9 r10 r11 r12 r13 r14 r15 r16 r17'.split()

resids = np.array([
     2.1847612857819,  2.1847612857819,  .36650228500366,  .36649596691132,
      .3702706694603,  .06275302171707,  1.6973150968552,  1.6973150968552,
     .60597640275955,  .60585051774979,  .62808901071548,  .23242343962193,
     4.7459564208984,  4.7459564208984,  3.0897438526154,  3.0483965873718,
     4.2380628585815,  3.7845225334167,  .02652905881405,  .02652905881405,
     .01329397037625,  .01329396758229,  .01330873556435,  .00667654490098,
     .92601269483566,  .92601269483566,  .60273587703705,  .60233747959137,
     .64300429821014,  .44648909568787,   .8528453707695,   .8528453707695,
     .72065913677216,  .71955502033234,   .7962681055069,   .7434441447258,
     .22364875674248,  .22364875674248,  .16446639597416,  .16445553302765,
     .16780391335487,  .12590345740318, -.26983660459518, -.26983660459518,
     -.1828535348177, -.18284019827843,  -.1791032999754, -.11887931078672,
    -.63495022058487, -.63495022058487, -.53598040342331, -.53542107343674,
    -.49657794833183, -.38836058974266, -1.7504912614822, -1.7504912614822,
    -1.2204585075378, -1.2154930830002, -1.0554916858673, -.63642859458923,
     -1.862185716629,  -1.862185716629, -1.2788465023041, -1.2732635736465,
    -1.1007128953934, -.65061664581299, -2.5617923736572, -2.5617923736572,
     -1.617108464241, -1.6071890592575, -1.3574055433273, -.71924245357513,
    -1.6135795116425, -1.6135795116425, -1.1469231843948, -1.1426799297333,
    -.99809640645981, -.61738300323486,  .22420094907284,  .22420094907284,
     .24363535642624,  .24356025457382,  .25454398989677,  .28899359703064,
     .06624934077263,  .06624934077263,  .06777309626341,  .06777160614729,
     .06855925172567,  .07094971090555,  .43318045139313,  .43318045139313,
     .51954871416092,  .51871728897095,  .57536894083023,  .76422989368439,
    -.89140218496323, -.89140218496323,  -.7140833735466,  -.7128586769104,
    -.64815932512283, -.47129172086716]).reshape(17,6)

resids_colnames = 'score_factor resid_response resid_anscombe resid_deviance resid_pearson resid_working'.split()

resids_rownames = 'r1 r2 r3 r4 r5 r6 r7 r8 r9 r10 r11 r12 r13 r14 r15 r16 r17'.split()


results_poisson_aweight_nonrobust = Bunch(
                params_table=params_table,
                params_table_colnames=params_table_colnames,
                params_table_rownames=params_table_rownames,
                cov=cov,
                cov_colnames=cov_colnames,
                cov_rownames=cov_rownames,
                infocrit=infocrit,
                infocrit_colnames=infocrit_colnames,
                infocrit_rownames=infocrit_rownames,
                predicted=predicted,
                predicted_colnames=predicted_colnames,
                predicted_rownames=predicted_rownames,
                resids=resids,
                resids_colnames=resids_colnames,
                resids_rownames=resids_rownames,
                **est
                )

est = dict(
           deviance = 23.34969514421719,
           dispers = 2.33496951442172,
           deviance_s = 23.34969514421719,
           dispers_s = 2.33496951442172,
           deviance_p = 30.06164170990202,
           dispers_p = 3.006164170990202,
           deviance_ps = 30.06164170990202,
           dispers_ps = 3.006164170990202,
           bic = -4.982438296344967,
           nbml = 0,
           N = 17,
           ic = 3,
           k = 7,
           k_eq = 1,
           k_dv = 1,
           converged = 1,
           k_autoCns = 0,
           ll = -52.96941847346162,
           chi2 = 356.6637749656061,
           p = 5.72458312679e-74,
           rc = 0,
           aic = 7.055225702760191,
           rank = 7,
           canonical = 1,
           power = 0,
           df_m = 6,
           df = 10,
           vf = 1,
           phi = 1,
           k_eq_model = 0,
           properties = "b V",
           depvar = "executions",
           which = "max",
           technique = "nr",
           singularHmethod = "m-marquardt",
           ml_method = "e2",
           crittype = "log pseudolikelihood",
           user = "glim_lf",
           title = "Generalized linear models",
           opt = "moptimize",
           chi2type = "Wald",
           wtype = "pweight",
           wexp = "= fweight",
           link = "glim_l03",
           varfunc = "glim_v3",
           m = "1",
           a = "1",
           oim = "oim",
           opt1 = "ML",
           varfuncf = "u",
           varfunct = "Poisson",
           linkf = "ln(u)",
           linkt = "Log",
           vcetype = "Robust",
           hac_lag = "15",
           marginsok = "default",
           marginsnotok = "stdp Anscombe Cooksd Deviance Hat Likelihood Pearson Response Score Working ADJusted STAndardized STUdentized MODified",
           predict = "glim_p",
           cmd = "glm",
           cmdline = "glm executions income perpoverty perblack LN_VC100k96 south degree [pweight=fweight], family(poisson)",
          )

params_table = np.array([
     .00025343868829,   .0000298866597,  8.4799937786829,  2.252059827e-17,
     .00019486191167,  .00031201546491, np.nan,  1.9599639845401,
                   0,  .09081422305585,  .08414617969117,  1.0792435662456,
     .28047916301946, -.07410925857549,  .25573770468718, np.nan,
     1.9599639845401,                0, -.09416451429381,  .01946961498728,
    -4.8364856909253,  1.321547815e-06, -.13232425846174, -.05600477012587,
    np.nan,  1.9599639845401,                0,  .27652273809506,
     .36112179485191,  .76573261995571,  .44383541350407, -.43126297384714,
     .98430845003726, np.nan,  1.9599639845401,                0,
      2.239890838384,  .43098853454849,  5.1971007551989,  2.024206636e-07,
     1.3951688329193,  3.0846128438487, np.nan,  1.9599639845401,
                   0, -18.842583191417,  4.5147658917489, -4.1735460139479,
     .00002998950578, -27.691361737874, -9.9938046449589, np.nan,
     1.9599639845401,                0, -6.5630017977416,  3.3999612612355,
     -1.930316639948,   .0535676165153, -13.226803418595,  .10079982311137,
    np.nan,  1.9599639845401,                0]).reshape(7,9)

params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()

params_table_rownames = 'income perpoverty perblack LN_VC100k96 south degree _cons'.split()

cov = np.array([
     8.932124278e-10,  1.512127962e-06,  1.877263788e-07, -4.562869239e-06,
    -2.023379829e-06, -.00001228516761, -.00002423071544,  1.512127962e-06,
     .00708057955662,  .00028427703202,  -.0019549511748, -.00596332288528,
     .20022061835302, -.18678265108673,  1.877263788e-07,  .00028427703202,
     .00037906590775, -.00453407701816, -.00623061980467, -.04659404972535,
     .02694184589715, -4.562869239e-06,  -.0019549511748, -.00453407701816,
     .13040895071706,   .0836259691825,  .89260578257395, -.82275604425197,
    -2.023379829e-06, -.00596332288528, -.00623061980467,   .0836259691825,
     .18575111691225,  1.0698498854979, -.64859219982217, -.00001228516761,
     .20022061835302, -.04659404972535,  .89260578257395,  1.0698498854979,
     20.383111057299, -12.482192460755, -.00002423071544, -.18678265108673,
     .02694184589715, -.82275604425197, -.64859219982217, -12.482192460755,
     11.559736577902]).reshape(7,7)

cov_colnames = 'income perpoverty perblack LN_VC100k96 south degree _cons'.split()

cov_rownames = 'income perpoverty perblack LN_VC100k96 south degree _cons'.split()

infocrit = np.array([
                  17, np.nan, -52.969418473462,                7,
     119.93883694692,  125.77133035532])

infocrit_colnames = 'N ll0 ll df AIC BIC'.split()

infocrit_rownames = '.'.split()

predicted = np.array([
     34.815238952637,  .06858423352242,  7.3026847839355,  .25687274336815,
     1.2540435791016,  .41320022940636,  3.9734709262848,  .16020278632641,
     2.0739872455597,  .22170753777027,  1.1471545696259,  .51121062040329,
     1.7763512134552,   .2167394310236,  2.2698366641998,   .2456086575985,
     1.6349502801895,  .25546172261238,  2.7504913806915,   .4417819082737,
      2.862185716629,  .61734634637833,  3.5617923736572,  .51518148183823,
     2.6135795116425,  .34006628394127,    .775799036026,    .292076587677,
     .93375068902969,  .39795544743538,  .56681954860687,  .31529840826988,
     1.8914022445679,  .26116076111794]).reshape(17,2)

predicted_colnames = 'predict_mu predict_linpred_std'.split()

predicted_rownames = 'r1 r2 r3 r4 r5 r6 r7 r8 r9 r10 r11 r12 r13 r14 r15 r16 r17'.split()

resids = np.array([
     2.1847612857819,  2.1847612857819,  .36650228500366,  .36649596691132,
      .3702706694603,  .06275302171707,  1.6973150968552,  1.6973150968552,
     .60597640275955,  .60585051774979,  .62808901071548,  .23242343962193,
     4.7459564208984,  4.7459564208984,  3.0897438526154,  3.0483965873718,
     4.2380628585815,  3.7845225334167,  .02652905881405,  .02652905881405,
     .01329397037625,  .01329396758229,  .01330873556435,  .00667654490098,
     .92601269483566,  .92601269483566,  .60273587703705,  .60233747959137,
     .64300429821014,  .44648909568787,   .8528453707695,   .8528453707695,
     .72065913677216,  .71955502033234,   .7962681055069,   .7434441447258,
     .22364875674248,  .22364875674248,  .16446639597416,  .16445553302765,
     .16780391335487,  .12590345740318, -.26983660459518, -.26983660459518,
     -.1828535348177, -.18284019827843,  -.1791032999754, -.11887931078672,
    -.63495022058487, -.63495022058487, -.53598040342331, -.53542107343674,
    -.49657794833183, -.38836058974266, -1.7504912614822, -1.7504912614822,
    -1.2204585075378, -1.2154930830002, -1.0554916858673, -.63642859458923,
     -1.862185716629,  -1.862185716629, -1.2788465023041, -1.2732635736465,
    -1.1007128953934, -.65061664581299, -2.5617923736572, -2.5617923736572,
     -1.617108464241, -1.6071890592575, -1.3574055433273, -.71924245357513,
    -1.6135795116425, -1.6135795116425, -1.1469231843948, -1.1426799297333,
    -.99809640645981, -.61738300323486,  .22420094907284,  .22420094907284,
     .24363535642624,  .24356025457382,  .25454398989677,  .28899359703064,
     .06624934077263,  .06624934077263,  .06777309626341,  .06777160614729,
     .06855925172567,  .07094971090555,  .43318045139313,  .43318045139313,
     .51954871416092,  .51871728897095,  .57536894083023,  .76422989368439,
    -.89140218496323, -.89140218496323,  -.7140833735466,  -.7128586769104,
    -.64815932512283, -.47129172086716]).reshape(17,6)

resids_colnames = 'score_factor resid_response resid_anscombe resid_deviance resid_pearson resid_working'.split()

resids_rownames = 'r1 r2 r3 r4 r5 r6 r7 r8 r9 r10 r11 r12 r13 r14 r15 r16 r17'.split()


results_poisson_pweight_nonrobust = Bunch(
                params_table=params_table,
                params_table_colnames=params_table_colnames,
                params_table_rownames=params_table_rownames,
                cov=cov,
                cov_colnames=cov_colnames,
                cov_rownames=cov_rownames,
                infocrit=infocrit,
                infocrit_colnames=infocrit_colnames,
                infocrit_rownames=infocrit_rownames,
                predicted=predicted,
                predicted_colnames=predicted_colnames,
                predicted_rownames=predicted_rownames,
                resids=resids,
                resids_colnames=resids_colnames,
                resids_rownames=resids_rownames,
                **est
                )

est = dict(
           k_eq_model = 0,
           phi = 1,
           vf = 1,
           df = 10,
           df_m = 6,
           power = 0,
           canonical = 1,
           rank = 7,
           aic = 4.579685683305704,
           rc = 0,
           p = 5.09268495340e-76,
           chi2 = 366.2131475852884,
           ll = -31.92732830809848,
           k_autoCns = 0,
           converged = 1,
           k_dv = 1,
           k_eq = 1,
           k = 7,
           ic = 3,
           N = 17,
           nbml = 0,
           bic = -9.740492454486454,
           dispers_ps = 2.475374834715614,
           deviance_ps = 24.75374834715614,
           dispers_p = 2.475374834715614,
           deviance_p = 24.75374834715614,
           dispers_s = 1.859164098607571,
           deviance_s = 18.59164098607571,
           dispers = 1.859164098607571,
           deviance = 18.59164098607571,
           cmdline = "glm executions income perpoverty perblack LN_VC100k96 south degree, family(poisson) vce(robust)",
           cmd = "glm",
           predict = "glim_p",
           marginsnotok = "stdp Anscombe Cooksd Deviance Hat Likelihood Pearson Response Score Working ADJusted STAndardized STUdentized MODified",
           marginsok = "default",
           hac_lag = "15",
           vcetype = "Robust",
           vce = "robust",
           linkt = "Log",
           linkf = "ln(u)",
           varfunct = "Poisson",
           varfuncf = "u",
           opt1 = "ML",
           oim = "oim",
           a = "1",
           m = "1",
           varfunc = "glim_v3",
           link = "glim_l03",
           chi2type = "Wald",
           opt = "moptimize",
           title = "Generalized linear models",
           user = "glim_lf",
           crittype = "log pseudolikelihood",
           ml_method = "e2",
           singularHmethod = "m-marquardt",
           technique = "nr",
           which = "max",
           depvar = "executions",
           properties = "b V",
          )

params_table = np.array([
     .00026110166569,  .00003534474167,  7.3872845963787,  1.498576223e-13,
     .00019182724497,   .0003303760864, np.nan,  1.9599639845401,
                   0,  .07781804809828,  .09819599835909,  .79247677500784,
     .42808272865983, -.11464257211148,  .27027866830805, np.nan,
     1.9599639845401,                0, -.09493110013466,  .01944446025221,
    -4.8821668950083,  1.049263903e-06, -.13304154192782,  -.0568206583415,
    np.nan,  1.9599639845401,                0,  .29693462055586,
     .34917491559373,  .85038932436186,  .39510866948496, -.38743563831266,
     .98130487942439, np.nan,  1.9599639845401,                0,
     2.3011832004524,  .45717041903387,  5.0335347709405,  4.815174289e-07,
      1.405145644349,  3.1972207565559, np.nan,  1.9599639845401,
                   0, -18.722067603077,  4.5006120067298, -4.1598937155841,
     .00003183957242, -27.543105044656, -9.9010301614985, np.nan,
     1.9599639845401,                0, -6.8014789919532,    3.48445447794,
    -1.9519494471841,  .05094420680386, -13.630884274485,  .02792629057847,
    np.nan,  1.9599639845401,                0]).reshape(7,9)

params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()

params_table_rownames = 'income perpoverty perblack LN_VC100k96 south degree _cons'.split()

cov = np.array([
     1.249250764e-09,  2.158351725e-06,  1.068227835e-07, -5.170410321e-06,
    -5.047866044e-07, -.00001662944527, -.00004339679838,  2.158351725e-06,
     .00964245409374,  .00008635335196, -.00640596402935, -.00524426268669,
     .23390140895418, -.22653903184676,  1.068227835e-07,  .00008635335196,
      .0003780870345, -.00382751790532,  -.0064534643179, -.05137117620883,
     .02948709519544, -5.170410321e-06, -.00640596402935, -.00382751790532,
     .12192312167989,   .0907733380116,  .89729289134262, -.69004336039169,
    -5.047866044e-07, -.00524426268669,  -.0064534643179,   .0907733380116,
     .20900479203961,  .93952111535021, -.75843860743141, -.00001662944527,
     .23390140895418, -.05137117620883,  .89729289134262,  .93952111535021,
      20.25550843512, -12.691830440798, -.00004339679838, -.22653903184676,
     .02948709519544, -.69004336039169, -.75843860743141, -12.691830440798,
     12.141423008836]).reshape(7,7)

cov_colnames = 'income perpoverty perblack LN_VC100k96 south degree _cons'.split()

cov_rownames = 'income perpoverty perblack LN_VC100k96 south degree _cons'.split()

infocrit = np.array([
                  17, np.nan, -31.927328308098,                7,
     77.854656616197,   83.68715002459])

infocrit_colnames = 'N ll0 ll df AIC BIC'.split()

infocrit_rownames = '.'.split()

predicted = np.array([
     35.226364135742,  .05631958693266,  8.1965742111206,  .14089094102383,
     1.3118965625763,  .51714926958084,  3.6862981319427,  .20286601781845,
     2.0823004245758,  .27275583148003,  1.0650315284729,  .58616667985916,
     1.9260421991348,  .30098018050194,  2.4171404838562,  .34251752495766,
     1.8473218679428,  .29685723781586,  2.8643238544464,  .47364214062691,
     3.1211984157562,  .72507524490356,   3.338207244873,  .54493451118469,
     2.5269968509674,  .34425318241119,  .89725440740585,  .37162157893181,
     .97933322191238,  .50227928161621,  .53462094068527,  .40906101465225,
     1.9790935516357,  .33805811405182]).reshape(17,2)

predicted_colnames = 'predict_mu predict_linpred_std'.split()

predicted_rownames = 'r1 r2 r3 r4 r5 r6 r7 r8 r9 r10 r11 r12 r13 r14 r15 r16 r17'.split()


results_poisson_none_hc1 = Bunch(
                params_table=params_table,
                params_table_colnames=params_table_colnames,
                params_table_rownames=params_table_rownames,
                cov=cov,
                cov_colnames=cov_colnames,
                cov_rownames=cov_rownames,
                infocrit=infocrit,
                infocrit_colnames=infocrit_colnames,
                infocrit_rownames=infocrit_rownames,
                predicted=predicted,
                predicted_colnames=predicted_colnames,
                predicted_rownames=predicted_rownames,
                **est
                )

est = dict(
           k_eq_model = 0,
           phi = 1,
           vf = 1,
           df = 26,
           df_m = 6,
           power = 0,
           canonical = 1,
           rank = 7,
           aic = 3.634510210512826,
           rc = 0,
           p = 1.5690245831e-115,
           chi2 = 549.7874580263729,
           ll = -52.96941847346162,
           k_autoCns = 0,
           converged = 1,
           k_dv = 1,
           k_eq = 1,
           k = 7,
           ic = 3,
           N = 33,
           nbml = 0,
           bic = -67.5595014539113,
           dispers_ps = 1.156216988842385,
           deviance_ps = 30.06164170990202,
           dispers_p = 1.156216988842385,
           deviance_p = 30.06164170990202,
           dispers_s = .8980651978545075,
           deviance_s = 23.34969514421719,
           dispers = .8980651978545075,
           deviance = 23.34969514421719,
           cmdline = "glm executions income perpoverty perblack LN_VC100k96 south degree [fweight=fweight], family(poisson) vce(robust)",
           cmd = "glm",
           predict = "glim_p",
           marginsnotok = "stdp Anscombe Cooksd Deviance Hat Likelihood Pearson Response Score Working ADJusted STAndardized STUdentized MODified",
           marginsok = "default",
           hac_lag = "15",
           vcetype = "Robust",
           vce = "robust",
           linkt = "Log",
           linkf = "ln(u)",
           varfunct = "Poisson",
           varfuncf = "u",
           opt1 = "ML",
           oim = "oim",
           a = "1",
           m = "1",
           varfunc = "glim_v3",
           link = "glim_l03",
           wexp = "= fweight",
           wtype = "fweight",
           chi2type = "Wald",
           opt = "moptimize",
           title = "Generalized linear models",
           user = "glim_lf",
           crittype = "log pseudolikelihood",
           ml_method = "e2",
           singularHmethod = "m-marquardt",
           technique = "nr",
           which = "max",
           depvar = "executions",
           properties = "b V",
          )

params_table = np.array([
     .00025343868829,   .0000263369674,  9.6229259983619,  6.398464168e-22,
     .00020181918073,  .00030505819585, np.nan,  1.9599639845401,
                   0,  .09081422305585,  .07431850776812,  1.2219597215163,
     .22172285914198, -.05484737555444,  .23647582166613, np.nan,
     1.9599639845401,                0, -.09416451429381,  .01609416304158,
    -5.8508487860178,  4.890707145e-09, -.12570849421662, -.06262053437099,
    np.nan,  1.9599639845401,                0,  .27652273809506,
     .34481886883624,  .80193621372381,  .42258985672342,  -.3993098260138,
     .95235530220392, np.nan,  1.9599639845401,                0,
      2.239890838384,  .39682271484988,  5.6445630619491,  1.656012749e-08,
     1.4621326090308,  3.0176490677372, np.nan,  1.9599639845401,
                   0, -18.842583191417,  4.1473740870735, -4.5432562377589,
     5.539185130e-06, -26.971287032495, -10.713879350338, np.nan,
     1.9599639845401,                0, -6.5630017977416,  3.0810023455152,
    -2.1301515097173,  .03315910688542, -12.601655431235, -.52434816424841,
    np.nan,  1.9599639845401,                0]).reshape(7,9)

params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()

params_table_rownames = 'income perpoverty perblack LN_VC100k96 south degree _cons'.split()

cov = np.array([
     6.936358517e-10,  1.301395377e-06,  1.497821854e-07, -4.758016826e-06,
    -1.852598001e-06, -6.904571080e-06, -.00001327109619,  1.301395377e-06,
     .00552324059688,  .00014714335792, -.00376147485446, -.00118957690573,
     .15979100738539, -.13853266210904,  1.497821854e-07,  .00014714335792,
     .00025902208401, -.00418693954572, -.00513741847691, -.03987504442994,
     .02761179707845, -4.758016826e-06, -.00376147485446, -.00418693954572,
      .1189000523055,  .08682729933237,  .80541854027627, -.70545315416752,
    -1.852598001e-06, -.00118957690573, -.00513741847691,  .08682729933237,
     .15746826702083,  1.1366624064282, -.75098089879076, -6.904571080e-06,
     .15979100738539, -.03987504442994,  .80541854027627,  1.1366624064282,
     17.200711818129, -11.062121016981, -.00001327109619, -.13853266210904,
     .02761179707845, -.70545315416752, -.75098089879076, -11.062121016981,
       9.49257545307]).reshape(7,7)

cov_colnames = 'income perpoverty perblack LN_VC100k96 south degree _cons'.split()

cov_rownames = 'income perpoverty perblack LN_VC100k96 south degree _cons'.split()

infocrit = np.array([
                  33, np.nan, -52.969418473462,                7,
     119.93883694692,  130.41438987719])

infocrit_colnames = 'N ll0 ll df AIC BIC'.split()

infocrit_rownames = '.'.split()

predicted = np.array([
     34.815238952637,  .06608480215073,  7.3026847839355,  .23366995155811,
     1.2540435791016,  .39606991410255,  3.9734709262848,  .12350843846798,
     2.0739872455597,  .18263976275921,  1.1471545696259,  .39735752344131,
     1.7763512134552,  .17952646315098,  2.2698366641998,  .21028706431389,
     1.6349502801895,  .17675416171551,  2.7504913806915,  .42150634527206,
      2.862185716629,  .58209121227264,  3.5617923736572,  .49835306406021,
     2.6135795116425,   .2456089258194,    .775799036026,  .23251366615295,
     .93375068902969,  .35320028662682,  .56681954860687,  .26245352625847,
     1.8914022445679,  .20374123752117]).reshape(17,2)

predicted_colnames = 'predict_mu predict_linpred_std'.split()

predicted_rownames = 'r1 r2 r3 r4 r5 r6 r7 r8 r9 r10 r11 r12 r13 r14 r15 r16 r17'.split()


results_poisson_fweight_hc1 = Bunch(
                params_table=params_table,
                params_table_colnames=params_table_colnames,
                params_table_rownames=params_table_rownames,
                cov=cov,
                cov_colnames=cov_colnames,
                cov_rownames=cov_rownames,
                infocrit=infocrit,
                infocrit_colnames=infocrit_colnames,
                infocrit_rownames=infocrit_rownames,
                predicted=predicted,
                predicted_colnames=predicted_colnames,
                predicted_rownames=predicted_rownames,
                **est
                )

est = dict(
           k_eq_model = 0,
           phi = 1,
           vf = 1,
           df = 10,
           df_m = 6,
           power = 0,
           canonical = 1,
           rank = 7,
           aic = 4.033797198035106,
           rc = 0,
           p = 5.72458312675e-74,
           chi2 = 356.663774965618,
           ll = -27.28727618329841,
           k_autoCns = 0,
           converged = 1,
           k_dv = 1,
           k_eq = 1,
           k = 7,
           ic = 3,
           N = 17,
           nbml = 0,
           bic = -16.30350260869269,
           dispers_ps = 1.548630027479802,
           deviance_ps = 15.48630027479802,
           dispers_p = 1.548630027479802,
           deviance_p = 15.48630027479802,
           dispers_s = 1.202863083186947,
           deviance_s = 12.02863083186947,
           dispers = 1.202863083186947,
           deviance = 12.02863083186947,
           cmdline = "glm executions income perpoverty perblack LN_VC100k96 south degree [aweight=fweight], family(poisson) vce(robust)",
           cmd = "glm",
           predict = "glim_p",
           marginsnotok = "stdp Anscombe Cooksd Deviance Hat Likelihood Pearson Response Score Working ADJusted STAndardized STUdentized MODified",
           marginsok = "default",
           hac_lag = "15",
           vcetype = "Robust",
           vce = "robust",
           linkt = "Log",
           linkf = "ln(u)",
           varfunct = "Poisson",
           varfuncf = "u",
           opt1 = "ML",
           oim = "oim",
           a = "1",
           m = "1",
           varfunc = "glim_v3",
           link = "glim_l03",
           wexp = "= fweight",
           wtype = "aweight",
           chi2type = "Wald",
           opt = "moptimize",
           title = "Generalized linear models",
           user = "glim_lf",
           crittype = "log pseudolikelihood",
           ml_method = "e2",
           singularHmethod = "m-marquardt",
           technique = "nr",
           which = "max",
           depvar = "executions",
           properties = "b V",
          )

params_table = np.array([
     .00025343868829,   .0000298866597,  8.4799937786833,  2.252059827e-17,
     .00019486191167,  .00031201546491, np.nan,  1.9599639845401,
                   0,  .09081422305585,  .08414617969118,  1.0792435662455,
     .28047916301948,  -.0741092585755,  .25573770468719, np.nan,
     1.9599639845401,                0, -.09416451429381,  .01946961498728,
    -4.8364856909248,  1.321547815e-06, -.13232425846174, -.05600477012587,
    np.nan,  1.9599639845401,                0,  .27652273809507,
     .36112179485206,  .76573261995541,  .44383541350425, -.43126297384744,
     .98430845003758, np.nan,  1.9599639845401,                0,
      2.239890838384,   .4309885345485,  5.1971007551988,  2.024206636e-07,
     1.3951688329193,  3.0846128438488, np.nan,  1.9599639845401,
                   0, -18.842583191417,  4.5147658917496, -4.1735460139472,
     .00002998950578, -27.691361737876, -9.9938046449574, np.nan,
     1.9599639845401,                0, -6.5630017977417,  3.3999612612367,
    -1.9303166399474,  .05356761651539, -13.226803418597,  .10079982311369,
    np.nan,  1.9599639845401,                0]).reshape(7,9)

params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()

params_table_rownames = 'income perpoverty perblack LN_VC100k96 south degree _cons'.split()

cov = np.array([
     8.932124278e-10,  1.512127962e-06,  1.877263788e-07, -4.562869239e-06,
    -2.023379829e-06, -.00001228516761, -.00002423071544,  1.512127962e-06,
     .00708057955662,  .00028427703202, -.00195495117479, -.00596332288528,
      .2002206183531,  -.1867826510868,  1.877263788e-07,  .00028427703202,
     .00037906590775, -.00453407701816, -.00623061980468, -.04659404972537,
     .02694184589718, -4.562869239e-06, -.00195495117479, -.00453407701816,
     .13040895071718,  .08362596918255,  .89260578257483, -.82275604425296,
    -2.023379829e-06, -.00596332288528, -.00623061980468,  .08362596918255,
     .18575111691226,  1.0698498854982, -.64859219982256, -.00001228516761,
      .2002206183531, -.04659404972537,  .89260578257483,  1.0698498854982,
     20.383111057306, -12.482192460764, -.00002423071544,  -.1867826510868,
     .02694184589718, -.82275604425296, -.64859219982256, -12.482192460764,
      11.55973657791]).reshape(7,7)

cov_colnames = 'income perpoverty perblack LN_VC100k96 south degree _cons'.split()

cov_rownames = 'income perpoverty perblack LN_VC100k96 south degree _cons'.split()

infocrit = np.array([
                  17, np.nan, -27.287276183298,                7,
     68.574552366597,   74.40704577499])

infocrit_colnames = 'N ll0 ll df AIC BIC'.split()

infocrit_rownames = '.'.split()

predicted = np.array([
     34.815238952637,  .06858423352242,  7.3026847839355,  .25687274336815,
     1.2540435791016,  .41320022940636,  3.9734709262848,  .16020278632641,
     2.0739872455597,  .22170753777027,  1.1471545696259,  .51121062040329,
     1.7763512134552,   .2167394310236,  2.2698366641998,   .2456086575985,
     1.6349502801895,  .25546172261238,  2.7504913806915,   .4417819082737,
      2.862185716629,  .61734634637833,  3.5617923736572,  .51518148183823,
     2.6135795116425,  .34006628394127,    .775799036026,    .292076587677,
     .93375068902969,  .39795544743538,  .56681954860687,  .31529840826988,
     1.8914022445679,  .26116076111794]).reshape(17,2)

predicted_colnames = 'predict_mu predict_linpred_std'.split()

predicted_rownames = 'r1 r2 r3 r4 r5 r6 r7 r8 r9 r10 r11 r12 r13 r14 r15 r16 r17'.split()


results_poisson_aweight_hc1 = Bunch(
                params_table=params_table,
                params_table_colnames=params_table_colnames,
                params_table_rownames=params_table_rownames,
                cov=cov,
                cov_colnames=cov_colnames,
                cov_rownames=cov_rownames,
                infocrit=infocrit,
                infocrit_colnames=infocrit_colnames,
                infocrit_rownames=infocrit_rownames,
                predicted=predicted,
                predicted_colnames=predicted_colnames,
                predicted_rownames=predicted_rownames,
                **est
                )

est = dict(
           k_eq_model = 0,
           phi = 1,
           vf = 1,
           df = 10,
           df_m = 6,
           power = 0,
           canonical = 1,
           rank = 7,
           aic = 7.055225702760191,
           rc = 0,
           p = 5.72458312679e-74,
           chi2 = 356.6637749656061,
           ll = -52.96941847346162,
           k_autoCns = 0,
           converged = 1,
           k_dv = 1,
           k_eq = 1,
           k = 7,
           ic = 3,
           N = 17,
           nbml = 0,
           bic = -4.982438296344967,
           dispers_ps = 3.006164170990202,
           deviance_ps = 30.06164170990202,
           dispers_p = 3.006164170990202,
           deviance_p = 30.06164170990202,
           dispers_s = 2.33496951442172,
           deviance_s = 23.34969514421719,
           dispers = 2.33496951442172,
           deviance = 23.34969514421719,
           cmdline = "glm executions income perpoverty perblack LN_VC100k96 south degree [pweight=fweight], family(poisson) vce(robust)",
           cmd = "glm",
           predict = "glim_p",
           marginsnotok = "stdp Anscombe Cooksd Deviance Hat Likelihood Pearson Response Score Working ADJusted STAndardized STUdentized MODified",
           marginsok = "default",
           hac_lag = "15",
           vcetype = "Robust",
           vce = "robust",
           linkt = "Log",
           linkf = "ln(u)",
           varfunct = "Poisson",
           varfuncf = "u",
           opt1 = "ML",
           oim = "oim",
           a = "1",
           m = "1",
           varfunc = "glim_v3",
           link = "glim_l03",
           wexp = "= fweight",
           wtype = "pweight",
           chi2type = "Wald",
           opt = "moptimize",
           title = "Generalized linear models",
           user = "glim_lf",
           crittype = "log pseudolikelihood",
           ml_method = "e2",
           singularHmethod = "m-marquardt",
           technique = "nr",
           which = "max",
           depvar = "executions",
           properties = "b V",
          )

params_table = np.array([
     .00025343868829,   .0000298866597,  8.4799937786829,  2.252059827e-17,
     .00019486191167,  .00031201546491, np.nan,  1.9599639845401,
                   0,  .09081422305585,  .08414617969117,  1.0792435662456,
     .28047916301946, -.07410925857549,  .25573770468718, np.nan,
     1.9599639845401,                0, -.09416451429381,  .01946961498728,
    -4.8364856909253,  1.321547815e-06, -.13232425846174, -.05600477012587,
    np.nan,  1.9599639845401,                0,  .27652273809506,
     .36112179485191,  .76573261995571,  .44383541350407, -.43126297384714,
     .98430845003726, np.nan,  1.9599639845401,                0,
      2.239890838384,  .43098853454849,  5.1971007551989,  2.024206636e-07,
     1.3951688329193,  3.0846128438487, np.nan,  1.9599639845401,
                   0, -18.842583191417,  4.5147658917489, -4.1735460139479,
     .00002998950578, -27.691361737874, -9.9938046449589, np.nan,
     1.9599639845401,                0, -6.5630017977416,  3.3999612612355,
     -1.930316639948,   .0535676165153, -13.226803418595,  .10079982311137,
    np.nan,  1.9599639845401,                0]).reshape(7,9)

params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()

params_table_rownames = 'income perpoverty perblack LN_VC100k96 south degree _cons'.split()

cov = np.array([
     8.932124278e-10,  1.512127962e-06,  1.877263788e-07, -4.562869239e-06,
    -2.023379829e-06, -.00001228516761, -.00002423071544,  1.512127962e-06,
     .00708057955662,  .00028427703202,  -.0019549511748, -.00596332288528,
     .20022061835302, -.18678265108673,  1.877263788e-07,  .00028427703202,
     .00037906590775, -.00453407701816, -.00623061980467, -.04659404972535,
     .02694184589715, -4.562869239e-06,  -.0019549511748, -.00453407701816,
     .13040895071706,   .0836259691825,  .89260578257395, -.82275604425197,
    -2.023379829e-06, -.00596332288528, -.00623061980467,   .0836259691825,
     .18575111691225,  1.0698498854979, -.64859219982217, -.00001228516761,
     .20022061835302, -.04659404972535,  .89260578257395,  1.0698498854979,
     20.383111057299, -12.482192460755, -.00002423071544, -.18678265108673,
     .02694184589715, -.82275604425197, -.64859219982217, -12.482192460755,
     11.559736577902]).reshape(7,7)

cov_colnames = 'income perpoverty perblack LN_VC100k96 south degree _cons'.split()

cov_rownames = 'income perpoverty perblack LN_VC100k96 south degree _cons'.split()

infocrit = np.array([
                  17, np.nan, -52.969418473462,                7,
     119.93883694692,  125.77133035532])

infocrit_colnames = 'N ll0 ll df AIC BIC'.split()

infocrit_rownames = '.'.split()

predicted = np.array([
     34.815238952637,  .06858423352242,  7.3026847839355,  .25687274336815,
     1.2540435791016,  .41320022940636,  3.9734709262848,  .16020278632641,
     2.0739872455597,  .22170753777027,  1.1471545696259,  .51121062040329,
     1.7763512134552,   .2167394310236,  2.2698366641998,   .2456086575985,
     1.6349502801895,  .25546172261238,  2.7504913806915,   .4417819082737,
      2.862185716629,  .61734634637833,  3.5617923736572,  .51518148183823,
     2.6135795116425,  .34006628394127,    .775799036026,    .292076587677,
     .93375068902969,  .39795544743538,  .56681954860687,  .31529840826988,
     1.8914022445679,  .26116076111794]).reshape(17,2)

predicted_colnames = 'predict_mu predict_linpred_std'.split()

predicted_rownames = 'r1 r2 r3 r4 r5 r6 r7 r8 r9 r10 r11 r12 r13 r14 r15 r16 r17'.split()


results_poisson_pweight_hc1 = Bunch(
                params_table=params_table,
                params_table_colnames=params_table_colnames,
                params_table_rownames=params_table_rownames,
                cov=cov,
                cov_colnames=cov_colnames,
                cov_rownames=cov_rownames,
                infocrit=infocrit,
                infocrit_colnames=infocrit_colnames,
                infocrit_rownames=infocrit_rownames,
                predicted=predicted,
                predicted_colnames=predicted_colnames,
                predicted_rownames=predicted_rownames,
                **est
                )

est = dict(
           k_eq_model = 0,
           vf = 1,
           df = 10,
           df_m = 6,
           power = 0,
           canonical = 1,
           rank = 7,
           aic = 4.579685683305704,
           rc = 0,
           p = 4.1950730971e-123,
           chi2 = 584.908728768987,
           ll = -31.92732830809848,
           N_clust = 9,
           k_autoCns = 0,
           converged = 1,
           k_dv = 1,
           k_eq = 1,
           k = 7,
           ic = 3,
           N = 17,
           nbml = 0,
           bic = -9.740492454486454,
           dispers_ps = 2.475374834715614,
           deviance_ps = 24.75374834715614,
           dispers_p = 2.475374834715614,
           deviance_p = 24.75374834715614,
           dispers_s = 1.859164098607571,
           deviance_s = 18.59164098607571,
           dispers = 1.859164098607571,
           deviance = 18.59164098607571,
           phi = 1,
           cmdline = "glm executions income perpoverty perblack LN_VC100k96 south degree, family(poisson) vce(cluster id)",
           cmd = "glm",
           predict = "glim_p",
           marginsnotok = "stdp Anscombe Cooksd Deviance Hat Likelihood Pearson Response Score Working ADJusted STAndardized STUdentized MODified",
           marginsok = "default",
           hac_lag = "15",
           vcetype = "Robust",
           vce = "cluster",
           linkt = "Log",
           linkf = "ln(u)",
           varfunct = "Poisson",
           varfuncf = "u",
           opt1 = "ML",
           clustvar = "id",
           oim = "oim",
           a = "1",
           m = "1",
           varfunc = "glim_v3",
           link = "glim_l03",
           chi2type = "Wald",
           opt = "moptimize",
           title = "Generalized linear models",
           user = "glim_lf",
           crittype = "log pseudolikelihood",
           ml_method = "e2",
           singularHmethod = "m-marquardt",
           technique = "nr",
           which = "max",
           depvar = "executions",
           properties = "b V",
          )

params_table = np.array([
     .00026110166569,  .00004098448535,  6.3707440379489,  1.881133617e-10,
     .00018077355048,   .0003414297809, np.nan,  1.9599639845401,
                   0,  .07781804809828,  .11602998752167,  .67067186475175,
     .50242959011024, -.14959654857083,   .3052326447674, np.nan,
     1.9599639845401,                0, -.09493110013466,  .02432927475974,
    -3.9019288931601,  .00009542919351, -.14261560243373, -.04724659783559,
    np.nan,  1.9599639845401,                0,  .29693462055586,
     .31774950884716,  .93449277587615,  .35004976070702, -.32584297288986,
     .91971221400158, np.nan,  1.9599639845401,                0,
     2.3011832004524,  .54874508731474,  4.1935376801516,  .00002746374324,
     1.2256625926223,  3.3767038082826, np.nan,  1.9599639845401,
                   0, -18.722067603077,  2.8106198749749, -6.6611880780372,
     2.716227723e-11, -24.230781332261, -13.213353873894, np.nan,
     1.9599639845401,                0, -6.8014789919532,  3.1571598785659,
    -2.1543029981246,  .03121641791743, -12.989398647377, -.61355933652912,
    np.nan,  1.9599639845401,                0]).reshape(7,9)

params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()

params_table_rownames = 'income perpoverty perblack LN_VC100k96 south degree _cons'.split()

cov = np.array([
     1.679728039e-09,  4.034336761e-06,  1.735749447e-07, -5.093610363e-06,
    -4.552211884e-06,  .00001563785418, -.00009230028034,  4.034336761e-06,
     .01346295800428,  .00110922683659, -.01950093608551, -.02957572460439,
     .08545644123676, -.23518641056668,  1.735749447e-07,  .00110922683659,
     .00059191361033, -.00720622811203, -.01195031391163, -.04317371228367,
     .03351736744645, -5.093610363e-06, -.01950093608551, -.00720622811203,
     .10096475037261,  .13375578883899,  .49763538443989, -.27357574414228,
    -4.552211884e-06, -.02957572460439, -.01195031391163,  .13375578883899,
     .30112117085206,  .65342245458316, -.47102547759356,  .00001563785418,
     .08545644123676, -.04317371228367,  .49763538443989,  .65342245458316,
     7.8995840816039, -6.5824964755966, -.00009230028034, -.23518641056668,
     .03351736744645, -.27357574414228, -.47102547759356, -6.5824964755966,
     9.9676584988266]).reshape(7,7)

cov_colnames = 'income perpoverty perblack LN_VC100k96 south degree _cons'.split()

cov_rownames = 'income perpoverty perblack LN_VC100k96 south degree _cons'.split()

infocrit = np.array([
                  17, np.nan, -31.927328308098,                7,
     77.854656616197,   83.68715002459])

infocrit_colnames = 'N ll0 ll df AIC BIC'.split()

infocrit_rownames = '.'.split()

predicted = np.array([
     35.226364135742,  .05941947177052,  8.1965742111206,  .09018591046333,
     1.3118965625763,  .53127920627594,  3.6862981319427,  .23996050655842,
     2.0823004245758,  .33554902672768,  1.0650315284729,  .53513532876968,
     1.9260421991348,  .32360115647316,  2.4171404838562,  .33078169822693,
     1.8473218679428,  .32581362128258,  2.8643238544464,  .46489810943604,
     3.1211984157562,  .71297109127045,   3.338207244873,  .58515930175781,
     2.5269968509674,  .42410242557526,  .89725440740585,  .40493285655975,
     .97933322191238,   .5560839176178,  .53462094068527,    .419488966465,
     1.9790935516357,   .3438538312912]).reshape(17,2)

predicted_colnames = 'predict_mu predict_linpred_std'.split()

predicted_rownames = 'r1 r2 r3 r4 r5 r6 r7 r8 r9 r10 r11 r12 r13 r14 r15 r16 r17'.split()


results_poisson_none_clu1 = Bunch(
                params_table=params_table,
                params_table_colnames=params_table_colnames,
                params_table_rownames=params_table_rownames,
                cov=cov,
                cov_colnames=cov_colnames,
                cov_rownames=cov_rownames,
                infocrit=infocrit,
                infocrit_colnames=infocrit_colnames,
                infocrit_rownames=infocrit_rownames,
                predicted=predicted,
                predicted_colnames=predicted_colnames,
                predicted_rownames=predicted_rownames,
                **est
                )

est = dict(
           k_eq_model = 0,
           vf = 1,
           df = 26,
           df_m = 6,
           power = 0,
           canonical = 1,
           rank = 7,
           aic = 3.634510210512826,
           rc = 0,
           p = 6.87057569032e-91,
           chi2 = 435.380362705941,
           ll = -52.96941847346162,
           N_clust = 9,
           k_autoCns = 0,
           converged = 1,
           k_dv = 1,
           k_eq = 1,
           k = 7,
           ic = 3,
           N = 33,
           nbml = 0,
           bic = -67.5595014539113,
           dispers_ps = 1.156216988842385,
           deviance_ps = 30.06164170990202,
           dispers_p = 1.156216988842385,
           deviance_p = 30.06164170990202,
           dispers_s = .8980651978545075,
           deviance_s = 23.34969514421719,
           dispers = .8980651978545075,
           deviance = 23.34969514421719,
           phi = 1,
           cmdline = "glm executions income perpoverty perblack LN_VC100k96 south degree [fweight=fweight], family(poisson) vce(cluster id)",
           cmd = "glm",
           predict = "glim_p",
           marginsnotok = "stdp Anscombe Cooksd Deviance Hat Likelihood Pearson Response Score Working ADJusted STAndardized STUdentized MODified",
           marginsok = "default",
           hac_lag = "15",
           vcetype = "Robust",
           vce = "cluster",
           linkt = "Log",
           linkf = "ln(u)",
           varfunct = "Poisson",
           varfuncf = "u",
           opt1 = "ML",
           clustvar = "id",
           oim = "oim",
           a = "1",
           m = "1",
           varfunc = "glim_v3",
           link = "glim_l03",
           wexp = "= fweight",
           wtype = "fweight",
           chi2type = "Wald",
           opt = "moptimize",
           title = "Generalized linear models",
           user = "glim_lf",
           crittype = "log pseudolikelihood",
           ml_method = "e2",
           singularHmethod = "m-marquardt",
           technique = "nr",
           which = "max",
           depvar = "executions",
           properties = "b V",
          )

params_table = np.array([
     .00025343868829,   .0000293670276,  8.6300422274613,  6.132932700e-18,
     .00019588037186,  .00031099700472, np.nan,  1.9599639845401,
                   0,  .09081422305585,  .09800194027664,  .92665739881773,
     .35410444288802, -.10126605030142,  .28289449641311, np.nan,
     1.9599639845401,                0, -.09416451429381,  .02511206083893,
    -3.7497724658197,  .00017699509401, -.14338324911569, -.04494577947193,
    np.nan,  1.9599639845401,                0,  .27652273809506,
     .36749499886987,  .75245306451906,  .45177864537662, -.44375422418847,
     .99679970037859, np.nan,  1.9599639845401,                0,
      2.239890838384,  .51564197481271,   4.343887712395,  .00001399830855,
      1.229251138834,   3.250530537934, np.nan,  1.9599639845401,
                   0, -18.842583191417,  3.2292740757113, -5.8349284543976,
     5.381365332e-09,  -25.17184407602, -12.513322306813, np.nan,
     1.9599639845401,                0, -6.5630017977416,  3.1938260811459,
    -2.0549026875586,  .03988840483712, -12.822785889672, -.30321770581092,
    np.nan,  1.9599639845401,                0]).reshape(7,9)

params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()

params_table_rownames = 'income perpoverty perblack LN_VC100k96 south degree _cons'.split()

cov = np.array([
     8.624223101e-10,  2.413510691e-06,  3.123995891e-07, -4.358439015e-06,
    -8.084672085e-06, -4.785328653e-06, -.00003652286809,  2.413510691e-06,
     .00960438029799,  .00106422375754, -.00911884619892, -.03121758372723,
     .06803953530989, -.17715756048416,  3.123995891e-07,  .00106422375754,
     .00063061559958, -.00844230553011, -.01177586448603, -.05361546061036,
     .03844868195577, -4.358439015e-06, -.00911884619892, -.00844230553011,
     .13505257419436,  .14058853110927,  .86184257188631, -.74146699290106,
    -8.084672085e-06, -.03121758372723, -.01177586448603,  .14058853110927,
     .26588664618875,  .75712244813913, -.35118919402718, -4.785328653e-06,
     .06803953530989, -.05361546061036,  .86184257188631,  .75712244813913,
     10.428211056061, -8.3518020608948, -.00003652286809, -.17715756048416,
     .03844868195577, -.74146699290106, -.35118919402718, -8.3518020608948,
     10.200525036608]).reshape(7,7)

cov_colnames = 'income perpoverty perblack LN_VC100k96 south degree _cons'.split()

cov_rownames = 'income perpoverty perblack LN_VC100k96 south degree _cons'.split()

infocrit = np.array([
                  33, np.nan, -52.969418473462,                7,
     119.93883694692,  130.41438987719])

infocrit_colnames = 'N ll0 ll df AIC BIC'.split()

infocrit_rownames = '.'.split()

predicted = np.array([
     34.815238952637,  .07249507308006,  7.3026847839355,  .17909966409206,
     1.2540435791016,  .36725598573685,  3.9734709262848,   .1719862818718,
     2.0739872455597,  .27532628178596,  1.1471545696259,  .51580721139908,
     1.7763512134552,  .23559851944447,  2.2698366641998,  .21655206382275,
     1.6349502801895,  .27835717797279,  2.7504913806915,  .44458091259003,
      2.862185716629,  .54439353942871,  3.5617923736572,  .57089400291443,
     2.6135795116425,  .41426089406013,    .775799036026,  .35101860761642,
     .93375068902969,  .39217269420624,  .56681954860687,  .27232182025909,
     1.8914022445679,  .24083258211613]).reshape(17,2)

predicted_colnames = 'predict_mu predict_linpred_std'.split()

predicted_rownames = 'r1 r2 r3 r4 r5 r6 r7 r8 r9 r10 r11 r12 r13 r14 r15 r16 r17'.split()


results_poisson_fweight_clu1 = Bunch(
                params_table=params_table,
                params_table_colnames=params_table_colnames,
                params_table_rownames=params_table_rownames,
                cov=cov,
                cov_colnames=cov_colnames,
                cov_rownames=cov_rownames,
                infocrit=infocrit,
                infocrit_colnames=infocrit_colnames,
                infocrit_rownames=infocrit_rownames,
                predicted=predicted,
                predicted_colnames=predicted_colnames,
                predicted_rownames=predicted_rownames,
                **est
                )

est = dict(
           k_eq_model = 0,
           vf = 1,
           df = 10,
           df_m = 6,
           power = 0,
           canonical = 1,
           rank = 7,
           aic = 4.033797198035106,
           rc = 0,
           p = 6.87057569091e-91,
           chi2 = 435.3803627057688,
           ll = -27.28727618329841,
           N_clust = 9,
           k_autoCns = 0,
           converged = 1,
           k_dv = 1,
           k_eq = 1,
           k = 7,
           ic = 3,
           N = 17,
           nbml = 0,
           bic = -16.30350260869269,
           dispers_ps = 1.548630027479802,
           deviance_ps = 15.48630027479802,
           dispers_p = 1.548630027479802,
           deviance_p = 15.48630027479802,
           dispers_s = 1.202863083186947,
           deviance_s = 12.02863083186947,
           dispers = 1.202863083186947,
           deviance = 12.02863083186947,
           phi = 1,
           cmdline = "glm executions income perpoverty perblack LN_VC100k96 south degree [aweight=fweight], family(poisson) vce(cluster id)",
           cmd = "glm",
           predict = "glim_p",
           marginsnotok = "stdp Anscombe Cooksd Deviance Hat Likelihood Pearson Response Score Working ADJusted STAndardized STUdentized MODified",
           marginsok = "default",
           hac_lag = "15",
           vcetype = "Robust",
           vce = "cluster",
           linkt = "Log",
           linkf = "ln(u)",
           varfunct = "Poisson",
           varfuncf = "u",
           opt1 = "ML",
           clustvar = "id",
           oim = "oim",
           a = "1",
           m = "1",
           varfunc = "glim_v3",
           link = "glim_l03",
           wexp = "= fweight",
           wtype = "aweight",
           chi2type = "Wald",
           opt = "moptimize",
           title = "Generalized linear models",
           user = "glim_lf",
           crittype = "log pseudolikelihood",
           ml_method = "e2",
           singularHmethod = "m-marquardt",
           technique = "nr",
           which = "max",
           depvar = "executions",
           properties = "b V",
          )

params_table = np.array([
     .00025343868829,   .0000293670276,  8.6300422274633,  6.132932700e-18,
     .00019588037186,  .00031099700472, np.nan,  1.9599639845401,
                   0,  .09081422305585,  .09800194027665,  .92665739881771,
     .35410444288803, -.10126605030143,  .28289449641312, np.nan,
     1.9599639845401,                0, -.09416451429381,  .02511206083893,
    -3.7497724658192,  .00017699509401, -.14338324911569, -.04494577947192,
    np.nan,  1.9599639845401,                0,  .27652273809507,
     .36749499887001,  .75245306451881,  .45177864537677, -.44375422418873,
     .99679970037887, np.nan,  1.9599639845401,                0,
      2.239890838384,  .51564197481271,   4.343887712395,  .00001399830855,
      1.229251138834,   3.250530537934, np.nan,  1.9599639845401,
                   0, -18.842583191417,  3.2292740757119, -5.8349284543965,
     5.381365332e-09, -25.171844076021, -12.513322306812, np.nan,
     1.9599639845401,                0, -6.5630017977417,   3.193826081147,
     -2.054902687558,  .03988840483718, -12.822785889674, -.30321770580895,
    np.nan,  1.9599639845401,                0]).reshape(7,9)

params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()

params_table_rownames = 'income perpoverty perblack LN_VC100k96 south degree _cons'.split()

cov = np.array([
     8.624223101e-10,  2.413510691e-06,  3.123995891e-07, -4.358439015e-06,
    -8.084672085e-06, -4.785328653e-06, -.00003652286809,  2.413510691e-06,
     .00960438029799,  .00106422375754, -.00911884619892, -.03121758372723,
     .06803953530995,  -.1771575604842,  3.123995891e-07,  .00106422375754,
     .00063061559958, -.00844230553012, -.01177586448603, -.05361546061038,
     .03844868195581, -4.358439015e-06, -.00911884619892, -.00844230553012,
     .13505257419447,   .1405885311093,  .86184257188684, -.74146699290197,
    -8.084672085e-06, -.03121758372723, -.01177586448603,   .1405885311093,
     .26588664618875,  .75712244813928, -.35118919402768, -4.785328653e-06,
     .06803953530995, -.05361546061038,  .86184257188684,  .75712244813928,
     10.428211056065, -8.3518020609031, -.00003652286809,  -.1771575604842,
     .03844868195581, -.74146699290197, -.35118919402768, -8.3518020609031,
     10.200525036615]).reshape(7,7)

cov_colnames = 'income perpoverty perblack LN_VC100k96 south degree _cons'.split()

cov_rownames = 'income perpoverty perblack LN_VC100k96 south degree _cons'.split()

infocrit = np.array([
                  17, np.nan, -27.287276183298,                7,
     68.574552366597,   74.40704577499])

infocrit_colnames = 'N ll0 ll df AIC BIC'.split()

infocrit_rownames = '.'.split()

predicted = np.array([
     34.815238952637,  .07249507308006,  7.3026847839355,  .17909966409206,
     1.2540435791016,  .36725598573685,  3.9734709262848,   .1719862818718,
     2.0739872455597,  .27532628178596,  1.1471545696259,  .51580721139908,
     1.7763512134552,  .23559851944447,  2.2698366641998,  .21655206382275,
     1.6349502801895,  .27835714817047,  2.7504913806915,  .44458091259003,
      2.862185716629,  .54439353942871,  3.5617923736572,  .57089400291443,
     2.6135795116425,  .41426089406013,    .775799036026,  .35101860761642,
     .93375068902969,  .39217269420624,  .56681954860687,  .27232182025909,
     1.8914022445679,  .24083258211613]).reshape(17,2)

predicted_colnames = 'predict_mu predict_linpred_std'.split()

predicted_rownames = 'r1 r2 r3 r4 r5 r6 r7 r8 r9 r10 r11 r12 r13 r14 r15 r16 r17'.split()


results_poisson_aweight_clu1 = Bunch(
                params_table=params_table,
                params_table_colnames=params_table_colnames,
                params_table_rownames=params_table_rownames,
                cov=cov,
                cov_colnames=cov_colnames,
                cov_rownames=cov_rownames,
                infocrit=infocrit,
                infocrit_colnames=infocrit_colnames,
                infocrit_rownames=infocrit_rownames,
                predicted=predicted,
                predicted_colnames=predicted_colnames,
                predicted_rownames=predicted_rownames,
                **est
                )

est = dict(
           k_eq_model = 0,
           vf = 1,
           df = 10,
           df_m = 6,
           power = 0,
           canonical = 1,
           rank = 7,
           aic = 7.055225702760191,
           rc = 0,
           p = 6.87057569032e-91,
           chi2 = 435.380362705941,
           ll = -52.96941847346162,
           N_clust = 9,
           k_autoCns = 0,
           converged = 1,
           k_dv = 1,
           k_eq = 1,
           k = 7,
           ic = 3,
           N = 17,
           nbml = 0,
           bic = -4.982438296344967,
           dispers_ps = 3.006164170990202,
           deviance_ps = 30.06164170990202,
           dispers_p = 3.006164170990202,
           deviance_p = 30.06164170990202,
           dispers_s = 2.33496951442172,
           deviance_s = 23.34969514421719,
           dispers = 2.33496951442172,
           deviance = 23.34969514421719,
           phi = 1,
           cmdline = "glm executions income perpoverty perblack LN_VC100k96 south degree [pweight=fweight], family(poisson) vce(cluster id)",
           cmd = "glm",
           predict = "glim_p",
           marginsnotok = "stdp Anscombe Cooksd Deviance Hat Likelihood Pearson Response Score Working ADJusted STAndardized STUdentized MODified",
           marginsok = "default",
           hac_lag = "15",
           vcetype = "Robust",
           vce = "cluster",
           linkt = "Log",
           linkf = "ln(u)",
           varfunct = "Poisson",
           varfuncf = "u",
           opt1 = "ML",
           clustvar = "id",
           oim = "oim",
           a = "1",
           m = "1",
           varfunc = "glim_v3",
           link = "glim_l03",
           wexp = "= fweight",
           wtype = "pweight",
           chi2type = "Wald",
           opt = "moptimize",
           title = "Generalized linear models",
           user = "glim_lf",
           crittype = "log pseudolikelihood",
           ml_method = "e2",
           singularHmethod = "m-marquardt",
           technique = "nr",
           which = "max",
           depvar = "executions",
           properties = "b V",
          )

params_table = np.array([
     .00025343868829,   .0000293670276,  8.6300422274613,  6.132932700e-18,
     .00019588037186,  .00031099700472, np.nan,  1.9599639845401,
                   0,  .09081422305585,  .09800194027664,  .92665739881773,
     .35410444288802, -.10126605030142,  .28289449641311, np.nan,
     1.9599639845401,                0, -.09416451429381,  .02511206083893,
    -3.7497724658197,  .00017699509401, -.14338324911569, -.04494577947193,
    np.nan,  1.9599639845401,                0,  .27652273809506,
     .36749499886987,  .75245306451906,  .45177864537662, -.44375422418847,
     .99679970037859, np.nan,  1.9599639845401,                0,
      2.239890838384,  .51564197481271,   4.343887712395,  .00001399830855,
      1.229251138834,   3.250530537934, np.nan,  1.9599639845401,
                   0, -18.842583191417,  3.2292740757113, -5.8349284543976,
     5.381365332e-09,  -25.17184407602, -12.513322306813, np.nan,
     1.9599639845401,                0, -6.5630017977416,  3.1938260811459,
    -2.0549026875586,  .03988840483712, -12.822785889672, -.30321770581092,
    np.nan,  1.9599639845401,                0]).reshape(7,9)

params_table_colnames = 'b se z pvalue ll ul df crit eform'.split()

params_table_rownames = 'income perpoverty perblack LN_VC100k96 south degree _cons'.split()

cov = np.array([
     8.624223101e-10,  2.413510691e-06,  3.123995891e-07, -4.358439015e-06,
    -8.084672085e-06, -4.785328653e-06, -.00003652286809,  2.413510691e-06,
     .00960438029799,  .00106422375754, -.00911884619892, -.03121758372723,
     .06803953530989, -.17715756048416,  3.123995891e-07,  .00106422375754,
     .00063061559958, -.00844230553011, -.01177586448603, -.05361546061036,
     .03844868195577, -4.358439015e-06, -.00911884619892, -.00844230553011,
     .13505257419436,  .14058853110927,  .86184257188631, -.74146699290106,
    -8.084672085e-06, -.03121758372723, -.01177586448603,  .14058853110927,
     .26588664618875,  .75712244813913, -.35118919402718, -4.785328653e-06,
     .06803953530989, -.05361546061036,  .86184257188631,  .75712244813913,
     10.428211056061, -8.3518020608948, -.00003652286809, -.17715756048416,
     .03844868195577, -.74146699290106, -.35118919402718, -8.3518020608948,
     10.200525036608]).reshape(7,7)

cov_colnames = 'income perpoverty perblack LN_VC100k96 south degree _cons'.split()

cov_rownames = 'income perpoverty perblack LN_VC100k96 south degree _cons'.split()

infocrit = np.array([
                  17, np.nan, -52.969418473462,                7,
     119.93883694692,  125.77133035532])

infocrit_colnames = 'N ll0 ll df AIC BIC'.split()

infocrit_rownames = '.'.split()

predicted = np.array([
     34.815238952637,  .07249507308006,  7.3026847839355,  .17909966409206,
     1.2540435791016,  .36725598573685,  3.9734709262848,   .1719862818718,
     2.0739872455597,  .27532628178596,  1.1471545696259,  .51580721139908,
     1.7763512134552,  .23559851944447,  2.2698366641998,  .21655206382275,
     1.6349502801895,  .27835717797279,  2.7504913806915,  .44458091259003,
      2.862185716629,  .54439353942871,  3.5617923736572,  .57089400291443,
     2.6135795116425,  .41426089406013,    .775799036026,  .35101860761642,
     .93375068902969,  .39217269420624,  .56681954860687,  .27232182025909,
     1.8914022445679,  .24083258211613]).reshape(17,2)

predicted_colnames = 'predict_mu predict_linpred_std'.split()

predicted_rownames = 'r1 r2 r3 r4 r5 r6 r7 r8 r9 r10 r11 r12 r13 r14 r15 r16 r17'.split()


results_poisson_pweight_clu1 = Bunch(
                params_table=params_table,
                params_table_colnames=params_table_colnames,
                params_table_rownames=params_table_rownames,
                cov=cov,
                cov_colnames=cov_colnames,
                cov_rownames=cov_rownames,
                infocrit=infocrit,
                infocrit_colnames=infocrit_colnames,
                infocrit_rownames=infocrit_rownames,
                predicted=predicted,
                predicted_colnames=predicted_colnames,
                predicted_rownames=predicted_rownames,
                **est
                )

est = dict(
           rank = 7,
           ll_0 = -55.23556912834824,
           ll = -47.54122045581504,
           r2_a = .3528737432046668,
           rss = 267.3132086911238,
           mss = 393.6105745962962,
           rmse = 5.17023412130557,
           r2 = .5955460895029168,
           F = .7279778160729128,
           df_r = 10,
           df_m = 6,
           N = 17,
           cmdline = "regress executions income perpoverty perblack LN_VC100k96 south degree [aweight=fweight], vce(robust)",
           title = "Linear regression",
           marginsok = "XB default",
           vce = "robust",
           depvar = "executions",
           cmd = "regress",
           properties = "b V",
           predict = "regres_p",
           model = "ols",
           estat_cmd = "regress_estat",
           wexp = "= fweight",
           wtype = "aweight",
           vcetype = "Robust",
          )

params_table = np.array([
     .00177624355887,  .00100571734546,  1.7661458926668,  .10782432028789,
    -.00046463433267,   .0040171214504,               10,  2.2281388519863,
                   0,  .70240571372092,  .54986275700055,  1.2774200557835,
     .23031379083217,  -.5227648584123,  1.9275762858541,               10,
     2.2281388519863,                0, -.76566360596606,  .46482124106144,
    -1.6472216377583,  .13053265392051, -1.8013498724035,  .27002266047141,
                  10,  2.2281388519863,                0,  5.7915855647065,
     5.8518623033717,  .98969956305525,  .34566324660643, -7.2471761899099,
     18.830347319323,               10,  2.2281388519863,                0,
     13.018291494864,  7.3741002410906,  1.7654074489417,  .10795348742173,
     -3.412227750751,   29.44881074048,               10,  2.2281388519863,
                   0, -140.99921608421,  84.973820309491, -1.6593253730463,
     .12803894207791, -330.33268651749,  48.334254349065,               10,
     2.2281388519863,                0, -68.484290889814,  50.764306481463,
    -1.3490638528633,  .20706938025917,  -181.5942144553,  44.625632675673,
                  10,  2.2281388519863,                0]).reshape(7,9)

params_table_colnames = 'b se t pvalue ll ul df crit eform'.split()

params_table_rownames = 'income perpoverty perblack LN_VC100k96 south degree _cons'.split()

cov = np.array([
     1.011467379e-06,  .00038778854684, -.00038909911416,  .00356508765632,
      .0056952104088, -.07926157334067, -.04218673068644,  .00038778854684,
     .30234905153625, -.10112236243026,  .59175926747871,  1.4744074711876,
      -25.6203584288, -14.793319880623, -.00038909911416, -.10112236243026,
     .21605878614189, -2.3405630815795, -3.2257627901142,   31.66920792546,
     20.934058595259,  .00356508765632,  .59175926747871, -2.3405630815795,
     34.244292417623,  34.810403897967, -270.34292245471, -270.19382562804,
      .0056952104088,  1.4744074711876, -3.2257627901142,  34.810403897967,
     54.377354365652,  -414.2817137548, -324.24739845086, -.07926157334067,
      -25.6203584288,   31.66920792546, -270.34292245471,  -414.2817137548,
     7220.5501379896,  2907.4556071681, -.04218673068644, -14.793319880623,
     20.934058595259, -270.19382562804, -324.24739845086,  2907.4556071681,
     2577.0148125439]).reshape(7,7)

cov_colnames = 'income perpoverty perblack LN_VC100k96 south degree _cons'.split()

cov_rownames = 'income perpoverty perblack LN_VC100k96 south degree _cons'.split()

infocrit = np.array([
                  17, -55.235569128348, -47.541220455815,                7,
     109.08244091163,  114.91493432002])

infocrit_colnames = 'N ll0 ll df AIC BIC'.split()

infocrit_rownames = '.'.split()

predicted = np.array([
     23.018356323242,  11.030969619751,  7.6487560272217,  3.2376720905304,
     1.3298480510712,  2.4579885005951,  6.7120413780212,  2.8951823711395,
     .90416890382767,  2.1985862255096,  1.9608836174011,  2.5452246665955,
     4.6054129600525,  2.8738057613373,  2.9902882575989,  1.8505314588547,
     1.4887162446976,    1.47836124897,  5.9044842720032,  4.8891386985779,
     7.0818486213684,  4.6786789894104,  7.5460968017578,  5.5129766464233,
     4.1125593185425,  2.3989260196686, -2.7979807853699,  3.8943622112274,
    -1.4647831916809,  2.8729522228241, -3.5234127044678,  3.7701880931854,
     3.9779393672943,  1.9573417901993]).reshape(17,2)

predicted_colnames = 'predict_mu predict_std'.split()

predicted_rownames = 'r1 r2 r3 r4 r5 r6 r7 r8 r9 r10 r11 r12 r13 r14 r15 r16 r17'.split()


results_wls_aweight_robust = Bunch(
                params_table=params_table,
                params_table_colnames=params_table_colnames,
                params_table_rownames=params_table_rownames,
                cov=cov,
                cov_colnames=cov_colnames,
                cov_rownames=cov_rownames,
                infocrit=infocrit,
                infocrit_colnames=infocrit_colnames,
                infocrit_rownames=infocrit_rownames,
                predicted=predicted,
                predicted_colnames=predicted_colnames,
                predicted_rownames=predicted_rownames,
                **est
                )

est = dict(
           rank = 7,
           ll_0 = -55.23556912834824,
           ll = -47.54122045581504,
           r2_a = .3528737432046668,
           rss = 267.3132086911238,
           mss = 393.6105745962962,
           rmse = 5.17023412130557,
           r2 = .5955460895029168,
           F = 1.412187242235973,
           df_r = 8,
           df_m = 6,
           N = 17,
           N_clust = 9,
           cmdline = "regress executions income perpoverty perblack LN_VC100k96 south degree [aweight=fweight], vce(cluster id)",
           title = "Linear regression",
           marginsok = "XB default",
           vce = "cluster",
           depvar = "executions",
           cmd = "regress",
           properties = "b V",
           predict = "regres_p",
           model = "ols",
           estat_cmd = "regress_estat",
           wexp = "= fweight",
           wtype = "aweight",
           vcetype = "Robust",
           clustvar = "id",
          )

params_table = np.array([
     .00177624355887,  .00103574504038,  1.7149428571794,  .12469817836724,
    -.00061218878728,  .00416467590501,                8,  2.3060041352042,
                   0,  .70240571372092,  .64463869959516,  1.0896114585768,
     .30761438040884, -.78413379325815,     2.1889452207,                8,
     2.3060041352042,                0, -.76566360596606,  .50850811868177,
    -1.5057057652313,  .17056206446331, -1.9382854304311,  .40695821849901,
                   8,  2.3060041352042,                0,  5.7915855647065,
     6.2948340440059,  .92005373362009,   .3844480847801, -8.7243277711951,
     20.307498900608,                8,  2.3060041352042,                0,
     13.018291494864,  7.9526248350517,  1.6369804642972,  .14027059672576,
    -5.3204942604922,  31.357077250221,                8,  2.3060041352042,
                   0, -140.99921608421,  84.897180497105, -1.6608233071889,
     .13532738016362, -336.77246537771,  54.774033209288,                8,
     2.3060041352042,                0, -68.484290889814,  50.203382265366,
    -1.3641369923608,   .2096627597382, -184.25349799498,  47.284916215355,
                   8,  2.3060041352042,                0]).reshape(7,9)

params_table_colnames = 'b se t pvalue ll ul df crit eform'.split()

params_table_rownames = 'income perpoverty perblack LN_VC100k96 south degree _cons'.split()

cov = np.array([
     1.072767789e-06,  .00042569049255, -.00044272344175,  .00386796354086,
     .00653558563917, -.08376884119522, -.04513384476642,  .00042569049255,
     .41555905301573, -.07730648264729, -.34087330734824,  .82631440946934,
    -31.768811666606, -10.324414524804, -.00044272344175, -.07730648264729,
     .25858050676528, -2.8727606144729, -3.9481543148554,  35.836754991381,
     24.653552354067,  .00386796354086, -.34087330734824, -2.8727606144729,
     39.624935641576,  42.351437415382, -335.98208369348, -283.16728769825,
     .00653558563917,  .82631440946934, -3.9481543148554,  42.351437415382,
      63.24424176708, -502.21726015398, -366.49477518415, -.08376884119522,
    -31.768811666606,  35.836754991381, -335.98208369348, -502.21726015398,
      7207.531256358,  3532.1379707168, -.04513384476642, -10.324414524804,
     24.653552354067, -283.16728769825, -366.49477518415,  3532.1379707168,
     2520.3795908825]).reshape(7,7)

cov_colnames = 'income perpoverty perblack LN_VC100k96 south degree _cons'.split()

cov_rownames = 'income perpoverty perblack LN_VC100k96 south degree _cons'.split()

infocrit = np.array([
                  17, -55.235569128348, -47.541220455815,                7,
     109.08244091163,  114.91493432002])

infocrit_colnames = 'N ll0 ll df AIC BIC'.split()

infocrit_rownames = '.'.split()

predicted = np.array([
     23.018356323242,  11.727355003357,  7.6487560272217,  3.4638004302979,
     1.3298480510712,  2.1195623874664,  6.7120413780212,  2.8227334022522,
     .90416890382767,  2.2036759853363,  1.9608836174011,  2.0707910060883,
     4.6054129600525,  2.9022018909454,  2.9902882575989,  1.6939970254898,
     1.4887162446976,  1.8477793931961,  5.9044842720032,  4.8752007484436,
     7.0818486213684,     4.4365234375,  7.5460968017578,  5.6850047111511,
     4.1125593185425,  2.7407164573669, -2.7979807853699,  3.9614858627319,
    -1.4647831916809,  2.4376966953278, -3.5234127044678,  3.5529434680939,
     3.9779393672943,  1.7075037956238]).reshape(17,2)

predicted_colnames = 'predict_mu predict_std'.split()

predicted_rownames = 'r1 r2 r3 r4 r5 r6 r7 r8 r9 r10 r11 r12 r13 r14 r15 r16 r17'.split()


results_wls_aweight_clu1 = Bunch(
                params_table=params_table,
                params_table_colnames=params_table_colnames,
                params_table_rownames=params_table_rownames,
                cov=cov,
                cov_colnames=cov_colnames,
                cov_rownames=cov_rownames,
                infocrit=infocrit,
                infocrit_colnames=infocrit_colnames,
                infocrit_rownames=infocrit_rownames,
                predicted=predicted,
                predicted_colnames=predicted_colnames,
                predicted_rownames=predicted_rownames,
                **est
                )

est = dict(
           rank = 7,
           ll_0 = -107.2219871314995,
           ll = -92.28589853187629,
           r2_a = .5022105716958969,
           rss = 518.9021109886529,
           mss = 764.067585981045,
           rmse = 4.467412394167744,
           r2 = .5955460895029162,
           F = 1.835843414931295,
           df_r = 8,
           df_m = 6,
           N = 33,
           N_clust = 9,
           cmdline = "regress executions income perpoverty perblack LN_VC100k96 south degree [fweight=fweight], vce(cluster id)",
           title = "Linear regression",
           marginsok = "XB default",
           vce = "cluster",
           depvar = "executions",
           cmd = "regress",
           properties = "b V",
           predict = "regres_p",
           model = "ols",
           estat_cmd = "regress_estat",
           wexp = "= fweight",
           wtype = "fweight",
           vcetype = "Robust",
           clustvar = "id",
          )

params_table = np.array([
     .00177624355887,  .00090840849363,  1.9553357012053,  .08627786102497,
    -.00031855018389,  .00387103730162,                8,  2.3060041352042,
                   0,  .70240571372091,  .56538554103558,  1.2423482079757,
     .24928937729829, -.60137568189177,  2.0061871093336,                8,
     2.3060041352042,                0, -.76566360596606,  .44599112337258,
    -1.7167687109468,  .12435346910262, -1.7941209807276,  .26279376879547,
                   8,  2.3060041352042,                0,  5.7915855647065,
     5.5209346785031,  1.0490226568442,  .32482245151877, -6.9397126341137,
     18.522883763527,                8,  2.3060041352042,                0,
     13.018291494864,  6.9749133861223,   1.866444896759,  .09894610636006,
    -3.0658876162246,  29.102470605953,                8,  2.3060041352042,
                   0, -140.99921608421,  74.459752971542, -1.8936299202886,
     .09489418422765, -312.70371434287,  30.705282174445,                8,
     2.3060041352042,                0, -68.484290889814,  44.031279012175,
    -1.5553554751584,  .15847103736706, -170.02060237022,   33.05202059059,
                   8,  2.3060041352042,                0]).reshape(7,9)

params_table_colnames = 'b se t pvalue ll ul df crit eform'.split()

params_table_rownames = 'income perpoverty perblack LN_VC100k96 south degree _cons'.split()

cov = np.array([
     8.252059913e-07,  .00032745422504, -.00034055649365,  .00297535656989,
      .0050273735686, -.06443757015017, -.03471834212801,  .00032745422504,
     .31966081001209, -.05946652511329, -.26221023642171,  .63562646882257,
    -24.437547435849, -7.9418573267692, -.00034055649365, -.05946652511329,
     .19890808212714, -2.2098158572872,  -3.037041780658,  27.566734608754,
      18.96427104159,  .00297535656989, -.26221023642171, -2.2098158572872,
     30.480719724298,  32.578028781062, -258.44775668729, -217.82099053713,
      .0050273735686,  .63562646882257,  -3.037041780658,  32.578028781062,
     48.649416743908, -386.32096934921, -281.91905783396, -.06443757015017,
    -24.437547435849,  27.566734608754, -258.44775668729, -386.32096934921,
      5544.254812583,  2717.0292082435, -.03471834212801, -7.9418573267692,
      18.96427104159, -217.82099053713, -281.91905783396,  2717.0292082435,
      1938.753531448]).reshape(7,7)

cov_colnames = 'income perpoverty perblack LN_VC100k96 south degree _cons'.split()

cov_rownames = 'income perpoverty perblack LN_VC100k96 south degree _cons'.split()

infocrit = np.array([
                  33,  -107.2219871315, -92.285898531876,                7,
     198.57179706375,  209.04734999402])

infocrit_colnames = 'N ll0 ll df AIC BIC'.split()

infocrit_rownames = '.'.split()

predicted = np.array([
     23.018356323242,  10.285571098328,  7.6487560272217,  3.0379540920258,
     1.3298480510712,  1.8589791059494,  6.7120413780212,  2.4757008552551,
     .90416890382767,  1.9327516555786,  1.9608836174011,  1.8162038326263,
     4.6054129600525,  2.5453994274139,  2.9902882575989,   1.485733628273,
     1.4887162446976,  1.6206097602844,  5.9044842720032,  4.2758340835571,
     7.0818486213684,  3.8910882472992,  7.5460968017578,  4.9860787391663,
     4.1125593185425,  2.4037673473358, -2.7979807853699,  3.4744529724121,
    -1.4647831916809,  2.1380014419556, -3.5234127044678,  3.1161375045776,
     3.9779393672943,  1.4975799322128]).reshape(17,2)

predicted_colnames = 'predict_mu predict_std'.split()

predicted_rownames = 'r1 r2 r3 r4 r5 r6 r7 r8 r9 r10 r11 r12 r13 r14 r15 r16 r17'.split()


results_wls_fweight_clu1 = Bunch(
                params_table=params_table,
                params_table_colnames=params_table_colnames,
                params_table_rownames=params_table_rownames,
                cov=cov,
                cov_colnames=cov_colnames,
                cov_rownames=cov_rownames,
                infocrit=infocrit,
                infocrit_colnames=infocrit_colnames,
                infocrit_rownames=infocrit_rownames,
                predicted=predicted,
                predicted_colnames=predicted_colnames,
                predicted_rownames=predicted_rownames,
                **est
                )

est = dict(
           rank = 7,
           ll_0 = -55.23556912834824,
           ll = -47.54122045581504,
           r2_a = .3528737432046668,
           rss = 267.3132086911238,
           mss = 393.6105745962962,
           rmse = 5.17023412130557,
           r2 = .5955460895029168,
           F = 1.412187242235973,
           df_r = 8,
           df_m = 6,
           N = 17,
           N_clust = 9,
           cmdline = "regress executions income perpoverty perblack LN_VC100k96 south degree [pweight=fweight], vce(cluster id)",
           title = "Linear regression",
           marginsok = "XB default",
           vce = "cluster",
           depvar = "executions",
           cmd = "regress",
           properties = "b V",
           predict = "regres_p",
           model = "ols",
           estat_cmd = "regress_estat",
           wexp = "= fweight",
           wtype = "pweight",
           vcetype = "Robust",
           clustvar = "id",
          )

params_table = np.array([
     .00177624355887,  .00103574504038,  1.7149428571794,  .12469817836724,
    -.00061218878728,  .00416467590501,                8,  2.3060041352042,
                   0,  .70240571372092,  .64463869959516,  1.0896114585768,
     .30761438040884, -.78413379325815,     2.1889452207,                8,
     2.3060041352042,                0, -.76566360596606,  .50850811868177,
    -1.5057057652313,  .17056206446331, -1.9382854304311,  .40695821849901,
                   8,  2.3060041352042,                0,  5.7915855647065,
     6.2948340440059,  .92005373362009,   .3844480847801, -8.7243277711951,
     20.307498900608,                8,  2.3060041352042,                0,
     13.018291494864,  7.9526248350517,  1.6369804642972,  .14027059672576,
    -5.3204942604922,  31.357077250221,                8,  2.3060041352042,
                   0, -140.99921608421,  84.897180497105, -1.6608233071889,
     .13532738016362, -336.77246537771,  54.774033209288,                8,
     2.3060041352042,                0, -68.484290889814,  50.203382265366,
    -1.3641369923608,   .2096627597382, -184.25349799498,  47.284916215355,
                   8,  2.3060041352042,                0]).reshape(7,9)

params_table_colnames = 'b se t pvalue ll ul df crit eform'.split()

params_table_rownames = 'income perpoverty perblack LN_VC100k96 south degree _cons'.split()

cov = np.array([
     1.072767789e-06,  .00042569049255, -.00044272344175,  .00386796354086,
     .00653558563917, -.08376884119522, -.04513384476642,  .00042569049255,
     .41555905301573, -.07730648264729, -.34087330734824,  .82631440946934,
    -31.768811666606, -10.324414524804, -.00044272344175, -.07730648264729,
     .25858050676528, -2.8727606144729, -3.9481543148554,  35.836754991381,
     24.653552354067,  .00386796354086, -.34087330734824, -2.8727606144729,
     39.624935641576,  42.351437415382, -335.98208369348, -283.16728769825,
     .00653558563917,  .82631440946934, -3.9481543148554,  42.351437415382,
      63.24424176708, -502.21726015398, -366.49477518415, -.08376884119522,
    -31.768811666606,  35.836754991381, -335.98208369348, -502.21726015398,
      7207.531256358,  3532.1379707168, -.04513384476642, -10.324414524804,
     24.653552354067, -283.16728769825, -366.49477518415,  3532.1379707168,
     2520.3795908825]).reshape(7,7)

cov_colnames = 'income perpoverty perblack LN_VC100k96 south degree _cons'.split()

cov_rownames = 'income perpoverty perblack LN_VC100k96 south degree _cons'.split()

infocrit = np.array([
                  17, -55.235569128348, -47.541220455815,                7,
     109.08244091163,  114.91493432002])

infocrit_colnames = 'N ll0 ll df AIC BIC'.split()

infocrit_rownames = '.'.split()

predicted = np.array([
     23.018356323242,  11.727355003357,  7.6487560272217,  3.4638004302979,
     1.3298480510712,  2.1195623874664,  6.7120413780212,  2.8227334022522,
     .90416890382767,  2.2036759853363,  1.9608836174011,  2.0707910060883,
     4.6054129600525,  2.9022018909454,  2.9902882575989,  1.6939970254898,
     1.4887162446976,  1.8477793931961,  5.9044842720032,  4.8752007484436,
     7.0818486213684,     4.4365234375,  7.5460968017578,  5.6850047111511,
     4.1125593185425,  2.7407164573669, -2.7979807853699,  3.9614858627319,
    -1.4647831916809,  2.4376966953278, -3.5234127044678,  3.5529434680939,
     3.9779393672943,  1.7075037956238]).reshape(17,2)

predicted_colnames = 'predict_mu predict_std'.split()

predicted_rownames = 'r1 r2 r3 r4 r5 r6 r7 r8 r9 r10 r11 r12 r13 r14 r15 r16 r17'.split()


results_wls_pweight_clu1 = Bunch(
                params_table=params_table,
                params_table_colnames=params_table_colnames,
                params_table_rownames=params_table_rownames,
                cov=cov,
                cov_colnames=cov_colnames,
                cov_rownames=cov_rownames,
                infocrit=infocrit,
                infocrit_colnames=infocrit_colnames,
                infocrit_rownames=infocrit_rownames,
                predicted=predicted,
                predicted_colnames=predicted_colnames,
                predicted_rownames=predicted_rownames,
                **est
                )

